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Trick 'r Treat – Die Nacht der Schrecken ist ein US-amerikanisch-kanadischer Horrorfilm von Michael Dougherty aus dem Jahr , der jedoch erst auf. fenriswolf.se - Kaufen Sie Trick 'r Treat günstig ein. Qualifizierte Bestellungen werden kostenlos geliefert. Sie finden Rezensionen und Details zu einer. Michael Doughertys Horror-Komödie Trick'r Treat - Die Nacht der Schrecken erzählt vier ineinander verwobene Geschichten, die sich alle in einer unheimlich.​. Im Haloween Filmtipp sehen wir uns den moderne Feiertags Horror Klassiker Trick 'r Treat von Michael Dougherty an. Dan's Freakshow. You spend weeks begging them to let you trick-or-treat anyway. люб​лю РІСњВ¤ | Р’РљРѕРРтакте.

trick ’r treat

You spend weeks begging them to let you trick-or-treat anyway. We keep trying to make Halloween happen. It's not going to happen. Trick 'r Treat – Die Nacht der Schrecken ist ein US-amerikanisch-kanadischer Horrorfilm von Michael Dougherty aus dem Jahr , der jedoch erst auf. Im Haloween Filmtipp sehen wir uns den moderne Feiertags Horror Klassiker Trick 'r Treat von Michael Dougherty an. Dan's Freakshow. Note that the trigger source must be set to the channel to which the voltage signal is connected. Experience is important, but for senior leadership we should visit web page out those who can adapt to the situation no matter what it is. A view controller manages a set of views that make up a portion of filme kinder app's user interface. Belted slacks. Want it! Brian Cox. Aktuelle News zu click to see more Filmen. Ein Auto kann gerade noch bremsen, in welchem sich die Werwolfmädchen in ihrer menschlichen Gestalt befinden. Sie kommt jedoch nicht, da sie im Garten von einem unbekannten Angreifer alexandra neldel 2019 und getötet wird. Girls glam sitzt jedoch im einzigen Aufzug, der in Sicherheit führt, und lässt diesen verschlossen. Alberto Ghisi.

Unlike recent years, the Titans have one thing they can always count on going into a match: experienced senior leadership, with the Academy sporting seven members in the Class of It helps most of us have played with each other for three or four years.

We have a big senior class. Coach Barone said her new assistant coach, John Reidy, has been an invaluable addition to the team this season.

The Senior team had the toughest task of the day, facing an undefeated Longmeadow squad. Running a unique offensive set, Kowalski hit Michael Hastings with a touchdown pass in the first half and connected again with Jacob Ferris late in the game.

With their home schedule complete, Ludlow will play their final two regular season games on the road. The two teams traded scores to open the second half and knot the game at Unfortunately for the Lions, a Longmeadow interception would allow the visitors to take the lead for good.

Some bright spots for the Lions included the defensive play of Mason Marques, Adrian Leiper and Roniel Traveras, who chased down a Longmeadow breakaway runner to the 1-yard line and captivated the crowd with his own touchdown saving tackle.

The Junior team solidified their spot in the playoffs with a thrilling win. Whereas Longmeadow has tradition and history on their side, Ludlow has Jamonte Beckett, an exciting firstyear player who creates magic on the field.

Beckett rushed for two touchdowns in the first half, including a spectacular play during which he was swarmed by two Longmeadow defenders, lost the ball, picked it back up and continued on into the end zone.

Beckett also had one reception for a touchdown and one interception on defense. Sunday, Oct. In the 11th, he sent one from the right side that forced Diotalevi to come out in order to keep it away from other Pacers.

In the 12th, the ball was back on the other end and Goncalves sent a 20 yarder just to the right of the goal. Nick Dos Santos had a corner kick in the 15th.

More shots rang out for Ludlow with Rogowski making a diving stop in the 16th minute off a yarder by Garete.

A minute after that, Garete was wide left from 8 yards. Back to the Ludlow end, it was Albahadly with a shot on goal from 15 yards in the 20th minute and in the 24th, Doyle could not corral a crossing pass from Aymen Saady in front of the goal.

A minute later, Machado led Frangules with a pass, but his shot from The Tim Peterson is a sports correspondent for Turley Publications.

In the 28th minute, Rogowski came well out of the goal to pick up a loose ball. Machado was wide left from 30 yards. Jones took a free kick in the 33rd minute and it was right on goal, where Diotalevi made the save.

It was a similar situation in the 35th as Jacob Parker put a yarder on Rogowski. The Lions had one last shot in the 38th minute, when Frangules sent one through the crease and Garete followed with a yarder that went over the goal.

The Lions peppered Chicopee with 18 shots, while Diotalevi faced just eight, stopping seven. Nate Rosenthal is a sports correspondent for Turley Publications.

Bernardo, Died Sept. Omer M. Bernardo, 86, of Ludlow, passed into the great beyond Tuesday, Sept. Born Nov. Navy Seabees. He saw extensive service in the Pacific Theater.

He was a lifelong electrician and owner of Bernardo Electric. In the years after his retirement, he served as the Electrical Inspector for the Town of Ludlow.

Omer leaves many nieces, nephews and friends. Aloysius Cemetery, Berkshire Avenue, Springfield. For more information, please go to www.

The committee meets once a month throughout the year, taking summer months off, to coordinate and implement all aspects of local, state and federal emergency preparedness guidelines.

Keeping a wallet inventory will help you to provide information to all relevant financial and business industries to keep your accounts secure and lessen your exposure to identity theft.

All information will remain confidential. Route begins and ends in Palmer each day. Hours are p. Mondays, Tuesdays and Fridays.

Must have a valid drivers license, insurance and good driving record. You will be using your own vehicle. Please apply in person to fill out an application.

Turley Publications, Inc. Volunteers can be utilized for EDS and widespread medical emergencies as well. We performed a second EDS drill last year.

Both drills were performed in cooperation with the State Department of Public Health and representatives from surrounding communities.

I encourage everyone to please consider becoming a volunteer because no matter how many volunteers we have listed, not everyone can show up at the same time.

Pszeniczny said the Senior Center may expand on the Wallet Inventory Program in the future to include a program that will help to keep Internet information at the ready.

Most people save this information on their computers, but what if your computer crashes? Creating a hard copy inventory of all usernames and passwords will keep this information available and secure.

To find out more about the Wallet Inventory Program or to download a form, go to www. If this describes you, please send your resume to: Timothy D.

Jude for prayers answered. American Idol or TV late news. Plus, many more read local papers online. Newspapers, in all forms, are still the primary source for news in the U.

The subject of the hearing is a Variance of a Sign. Anthony W. The Local Emergency Planning Committee would like to announce the following information concerning meetings, planning and information storage.

Meetings of the Local Emergency Planning Committee are generally held at p. The public is invited to attend. Meeting notices are placed at the Ludlow Town Hall and should be checked prior to attending as some monthly meetings may be rescheduled.

The Committee has developed an integrated emergency response plan and these plans are available to be reviewed.

Additional material that is site specific for hazardous material storage is also available at the same location. These records may be viewed at the Ludlow Fire Department, Monday through Friday by appointment only from a.

Proper photo identification will be required. The subject of the meeting is: Clearing of the lot, construction of house with septic system in the front and the well in the rear.

Site plans, if applicable, are on file for inspection in the Planning Board Office. Please come and pray in the public square to ask Our Lady to intercede on behalf of our country because America truly needs her help.

Mike Ferreira will be hosting and providing the dinner and entertainment. Bar opens at 6 p. We will need reservations and check by Sept.

Admission is free. Bonsai workshops will be held from 10 a. Space is limited. To learn more go to www. Public invited.

All are welcome. Drop off times for donations will be Monday, Oct. Donations of gently used clothing for all ages and household items are greatly appreciated.

We will accept small electrical items in working order, but no TVs or electronics. Spaghetti with meat sauce and macaroni and cheese will be served, along with salad, bread and butter, coffee, tea and dessert.

Kids under four are FREE. Advance purchase of tickets is recommended. For more information or to purchase tickets, call the parsonage at Visitors are invited to enjoy many favorite Armenian dinners.

Both traditional Armenian and American baked goods will be featured. Take-out will be available by calling Raffle prizes.

Admission and parking are free. For more information, please call the Church office at Homer P. Thursday, Oct.

Friday, Oct. Saturday, Oct. Mass; a. Mass; 11 a. Mass; 5 p. Monday, Oct. Tuesday, Oct. Ludlow Rev. Please join us and introduce yourself if you are visiting!

Visit our website at www. The First Church in Ludlow is now open for prayer and meditation on Tuesdays from 4 to 6 p.

Please join us in solitude or in fellowship. Sunday mornings. Tuesday, p. Wednesday, 8 p. Thursday, 7 p. Jeffrey K.

Fellowship hour following Office Hours: Wednesday, 9 a. Christ the King Church 41 Warsaw Ave. Pastor Rev.

Raymond A. Polish ; 10 a. Barbaro at or Michelle Roderick Lussier at Immaculate Conception Church 24 Parker St.

English ; a. Polish ; 5 p. William Pomerleau, Pastor Please note: services for St. Tuesday: 6 p.

Confessions in Spanish; p. Adoration of Blessed Sacrament in Spanish Thursday: p. Reconciliation Schedule: Saturday to p. Harvest Bible Chapel Services are held at 10 a.

For more information, visit www. Douglas E. Fish Sunday Services: a. Sunday School for all Ages; a. Fellowship for all Ages; 11 a.

Sunday Worship Service. Thursday Evening at p. Gathering for prayer at the church. Sunday evenings at p. Leader is Justin Wenners. Classes for Young Women; Noon to 1 p.

Classes for Young Single Adult Men. A Novena to St. Peregrine, patron saint for those with cancer, takes place every Monday at p.

Cancer patients, cancer survivors, friends of cancer victims, and the general public are invited to attend. Children ages two to 12 must dress up to trick-or-treat on the zoo grounds.

Staff and volunteers will hand out free treats to young zoo-goers. Kids who participate will also receive a free bag of animal feed to include the animals in some Halloween fun.

Face painting; free coloring and activity books, and pumpkin painting. The 5K will be held Sunday, Oct. Social hour begins at a.

Bank Clothiers of Longmeadow. Luncheon choices are salmon, chicken francaise, roast pork loin. For reservations, call by Oct.

Proceeds will benefit scholarships and many community projects. Christmas crafts, knitted goods, bake sale, jewelry, attic treasures, clothing, pillows, handcrafted sewn items, raffles, auctions, used books, used toys and much more.

Take out is also available. Call This is a continuous serve buffet from a. Union Church of Christ is located at 51 Center St.

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Tiny Trotters Program. Licensed instructors. Excellent school horsesponies. How might we implement a power supply for creating a sudden step in voltage?

You may recall that the function generator can provide a square-wave output. This is essentially a DC source which periodically changes its voltage.

You can specify how often the voltage is changed by adjusting the frequency of the wave. By adjusting the DC-offset and amplitude you can adjust what voltages the source switches between.

To make it simple, adjust your supply so Vi switches between 0 V and 2 V. Laboratory 7. But what about the period? Since it is of interest to see the whole response, [LR 1] that is, until steady state is reached, we should estimate how long to set the period.

Recall from the pre-lab where you identified the steady-state and transient portions of Vo t.

When the transient portion goes to zero, we are at steady state. Of course, you can see that this will take an infinite amount of time. Hook up your function generator to the circuit and observe the [LR 2] output, Vo t on the oscilloscope.

Capture your output and label the steady-state and transient portions. Did you notice that you are measuring the voltage across the capacitor?

In- [LR 3] dicate what the capacitor is doing at each portion of the curve on your screen capture charging, discharging or staying the same.

Can you verify your time constant experimentally? Since the time constant is often a critical design parameter, we should quantify the uncertainty in your calculations and measurements and indicate the sources oscilloscope, capacitors, etc.

In the pre-lab you sketched your prediction of the time-dependent voltage across the resistor. Can you vindicate your Laboratory 7.

Do you see any differences? If so, why might they exist? Capture this result, label the steady-state and transient portions, and describe what is happening.

You may want to check the components section of the manual to find out how the switch works. Build the circuit shown in Figure 7.

It will be helpful to view the response on the oscilloscope. Estimating the time constant may help you to easily capture the output.

When you are ready, flip the switch from position A to position B and observe the result. In the pre-lab, you generated a general formula for vo.

Do your these calculations match the corresponding measured values? You may want to include a screen-shot to augment your claim in your report.

Now we will apply continuously changing, AC voltages to the circuit in Figure 7. Set Vi as a sine wave with peak-to-peak amplitude 5 V and zero DC offset.

In the pre-lab, you showed that by taking the output across the capacitor [LR 7] or resistor of Figure 7. Plot the data to show the trend and make sure you have enough points to do this sufficiently.

It may be helpful to note that conventionally, these plots have logarithmic axes. Keep this in mind when determining the range of frequencies to examine.

The frequency at which half-power is obtained is known as the cutoff fre- [LR 8] quency. This is an important number for any filter.

Indicate the cutoff frequency on your plot. To better understand where the power is being dissipated, consider Figure 7.

It is across these resistors that the power is dissipated. Repeat the above two steps for the high-pass filter.

The cutoff frequencies [LR 10] you just found should correspond to a gain that is 3 dB less than the maximum.

Your plots will have characteristic shapes. Think about what these shapes [LR 11] mean and suggest a practical use for both types of circuits.

First-Order Circuits 48 2. In the previous laboratory we looked at circuits with a single storage element, a capacitor.

In this laboratory we will investigate circuits which contain two storage elements. Such circuits are known as secondorder circuits.

We will look at a series RLC circuit which contains a resistor an inductor and a capacitor in series. We will see how, by varying the values of these three components, we can obtain three distinct voltage responses; underdamped, critically damped, and overdamped.

For this laboratory we will derive the necessary background in the Pre-lab section. Call this current i. Express the voltage across the resistor and the voltage across the inductor in terms of this current i.

The constant A should also depend on the input voltage V. Inductors: One: 0. One: 0. We will use the function generator to provide the voltage V to the circuit.

However, this circuit has a very low input impedance. To buffer the function generator from the RLC circuit, we will build a voltage follower.

Build the voltage follower shown in Figure 8. They filter out any AC component of the signal, and ensure that the voltage supplied to op-amp is steady.

Laboratory 8. Connect the function generator to vib and verify that the output at vob is as expected. Leave this circuit assembled.

Build the series RLC circuit shown in Figure 8. Use the output of the voltage follower, vob as V. Use both channels of the oscilloscope, displaying the square wave on one channel, and vC t on the other.

Adjust the frequency of the square wave so that the step response of the circuit is clearly visible. Is your system overdamped, underdamped, or critically damped?

Take a [LR 1] screenshot of the response. Second-Order Circuits 52 5. Calculate the resistance, Rcrit , required to make the system critically damped.

Compare this resistance with the value you calculated for Rcrit. Comment on any differences between the two values. Experiment with different values of R.

Notice how the step response changes [LR 4] as the resistance is varied. For what values of R is the system underdamped?

Take screenshots of these two responses. Now, switch the resistor and capacitor so that you can measure vR t , and [LR 5] set R so that the system is underdamped.

Take a screenshot of the response. Does this response make sense? Explain why or why not? Now rearrange the circuit so that you can measure the inductor voltage vL t [LR 6] and set R so that the system is overdamped.

In this part you will find a sample laboratory and the associated report to demonstrate the expected level of quality in analysis, writing, and presentation.

The report illustrates what is expected for your lab reports. The numbers on the right highlight the strong points and are explained on the following page.

For those that like to learn from mistakes, an example bad report is provided subsequently with a list common errors and those specific to the report.

You should organize your report in the following manner. This section briefly summarizes what you did and what you learned.

It comes from your own mind; it should be in your own words. PRE-LAB The pre-lab is essential since it ties theory to your experimental findings; it gives you educated expectations.

There are three steps. First, before the lab begins, complete a typed pre-lab that answers all the pre-lab questions in complete sentences.

Calculations may be handwritten and attached if done neatly. Second, give this to your TA at the start of the lab.

The TA will review it, sign it, and return it to you in the same session. To get credit for the pre-lab, attach the signed copy to your lab report.

Third, while preparing your lab report, make sure your pre-lab especially anything calculated is in agreement with your data.

If it is not, find the source s of the error s. Note: some disagreements are expected and you ought to be able to justify them. Grading Rubric 55 can connect theory to practice and how you can account for disparity.

Retain the [LR] numbering scheme used in the laboratory instructions. Again, your answers can be brief, but they should be in full sentences.

All plots and tables must have appropriate labels and captions. The following grading rubric will be used to evaluate your reports.

However, you should read the lab and use the sample lab report that has been written for this lab as a guide for your reports.

For example, an electron has negative charge, and a proton has positive charge. Now, negative charge attracts positive charge.

So, we must expend energy when we separate a negative charge from a positive one. Or, to think of it differently, in separating charge, we are storing energy.

Voltage v is the energy E per unit charge q stored in the separation. We write this as dE. If the current you calculated flows through the resistor for 10 seconds, how much charge has passed through the resistor.

Build the circuit shown in Figure Measure the voltage across, and current in, the resistor R2.

How do these [LR 1] measured quantities agree with the values you calculated in the pre-lab? Now set V to a triangle wave with a frequency of 2 kHz, a peak-to-peak amplitude of 5 V and zero DC offset.

Measure the voltage across R1 using the oscilloscope. Take a screen shot of [LR 2] the voltage as a function of time and include it in your report.

Measure the period of the waveform on your oscilloscope and compare it to that of the input. Section Observed AC results were shown to match expected values.

Under experimental uncertainty analysis, DC results were shown to fall within experimental uncertainty of expected values.

The total charge Q passing through R2 in 10 seconds can be found by integrating the current across that span of time.

The measurements made with the TX3 are shown in Table The relative uncertainty was found by writing the ratio of the absolute uncertainty to the measured value as a percentage.

Now the formulae from the pre-lab can be used to find the expected V2 and I. In these calculations RU x is the relative uncertainty of x and AU x is the absolute uncertainty of x.

Had they been a little off, it may have been due to the internal resistance of the multimeter and the power supply, providing unaccounted for resistances in the voltage divider.

The input voltage, V , and the voltage, V1 are shown in Figure The input [5] Section Example of a Good Report 61 amplitude and period are 5.

Across R1 the amplitude and period are mV and The abstract should be written after you conduct your experiment and in past tense.

Units are indicated for all values. Axes are clearly identified with labels, scales, and units. Figures are numbered with the lab report number and figure number, separated by a hyphen.

According to the lab manual p. This relative uncertainty RU is translated to absolute uncertainty AU by equation 1.

Therefore, it can be seen that the estimated resistances from deciphering the color bands and the measured resistances from multimeter reading converge to each other, when the AU is considered.

Our measurements are more suscepti- [8] 63 Section Example of a Bad Report 64 ble with this kind of handling error with low resistance.

The multimeter is trying to measure the voltage across the diode. This ensures that the voltage reference is zero prior to making measurements.

We did so by connecting the input clip to the ground clip, and adjusting the voltage at the mid-point on the oscilloscope screen. The signal from the grounded probe and adjusted scales displayed in channel 1 of the oscilloscope is shown in Figure The oscilloscope signal of a grounded probe in adjusted scales displayed in channel 1.

LR 7 [] Section Oscilloscope: 5. No, it does not make sense to use the amplitude tool. You can learn from their errors.

Show your work. Demonstrate clear thinking. Remember, the abstract should cover four things: 1 what was done, 2 what was observed, 3 what were the conclusions, and 4 what were the lessons from the observations and conclusions.

In the case of lab 1, some of the main points include working with the lab components and equipments and knowing their uncertainties.

Knowing these objectives, what experiments were done to help achieve these learning objectives? Use your own words. According to the manual, a proper table is numbered with the lab report number and the table number, separated by a hyphen.

Also, units are indicated for all values. So, please elaborate on what was done e. In this case, it would be a good idea to include a formula to explain how the color bands are translated to resistance values.

Example of a Bad Report 69 [4] Here, the relative uncertainty was converted to absolute uncertainty without showing work.

While extra work was done, it was not done properly. You should always show work. In this case, all that is needed is equation 1 and a short description that equation 1 was used to convert relative uncertainty to absolute uncertainty.

In this case, a short sentence would do the trick. Do they converge when the uncertainties are considered? If not, why not? Overall, this answer to LR2 shows what the expected answers look like.

Also, there is no calculation. A proper answer is written in complete sentences and it describes what was done and what was observed.

Also, the answer did not include the uncertainty as the manual instructed. The second answer, while written in a complete sentence, does not answer the question at an analytical level as expected.

The LR question must also be answered explicitly. The uncertainties of the different equipments can be found in the manual.

How far off is too far off? Do they match when the uncertainties are considered? The total accuracy of each device is determined by the combination of the percent error and offset.

Percent error indicates gives the maximum amount that your measured value can deviate from the true value, as a percentage of the measured value.

For example, a reading of 3. This means that due to this quantity, your measurement with error would be 3. Accuracy of Equipment 72 The offset column gives how maximum deviation of the least significant digit of your measurement.

For example, a reading of It would have an offset of 2. This means the least significant digit i. In other words, the offset error would be Total accuracy can then be found by combining the percent error and offset.

As an example, say the TX3 measures Its total accuracy is So, the measurement should be reported as Note that we do not report error beyond the least significant digit.

So, for that reason we report, The following is a summary of the components and the lab, and what they look like. Familiarizing yourself with this set could improve the efficiency of your laboratory execution.

Note the color bands on these. Some have 4 bands others have 5. These bands indicate the resistance and the tolerance.

These resistors are able to dissipate 0. For high-power applications, more robust resistors are required. Some types like the one on the upper left indicate which side is the negative terminal.

Hooking this type up incorrectly can ruin your circuit. Laboratory Components 74 Diodes. The diode permits current to exit through the silver-banded end only.

BNC cables. These cables are used for connecting devices such as oscilloscopes and function generators. Banana cables. These cables are used for connecting to devices with the appropriate plugs.

For example, multimeters. Laboratory Components 75 Potentiometers. The center pin is connected to the wheel such that the resistance between left and center pin can be adjusted from zero to the maximum.

The resistance between the center and right pins is simply the remaining portion of the resistance. Typical op-amps chips, and the pin diagram for the LMC.

Toggle switch. The toggle switch connects the green wire in the middle to the orange wire on the left when toggled to the right or to the brown wire on the right when toggled to the left.

Section 15 How to Read Resistor Color Codes The most common electrical component found in almost every electrical circuit is the resistor.

The type we will use in the lab is the -watt axial-lead resistor. Resistors with a five-band resistance code usually have higher precision 0.

The colors used for the bands are listed with their respective values in the color code chart in Table Table The tolerance band is the last band on the resistor and is usually spaced a little further away from the other bands.

To determine the value of a resistor, 76 Section Figure Then, read the color bands from left to right.

The first two bands of the four-band convention and the first three bands of the five-band convention are the significant digits of the resistor value.

The second to last band indicates the multiplier. For the five-band resistor shown in Figure A four-band resistor is read in the same way, except that there are only two significant digits instead of three.

Thus, the value of the resistor in Figure Section 16 How to Read Capacitor Values Reading capacitor values is a little more cryptic than reading resistor values as there are a few different conventions for marking capacitor values.

Here, we will explain the most commonly seen marking schemes. Smaller capacitors normally use a two- or three-digit code. In the two-digit marking scheme, the two digits simply indicate the capacitor value to two significant digits.

The three-digit convention is somewhat similar to the resistor coding scheme. The first two numbers are the first and second significant digits and the third is a multiplier code.

Generally, the third digit tells you how many zeros to write after the first two digits but there are a few exceptions.

Sometimes, following the number code is a capital letter a Multiplier code for three-digit convention. Third Digit 0 1 2 3 4 5 6 7 8 9 Multiplier 1 10 1, 10, , not used not used 0.

The tolerance code is given in Table So, a 78 Section However, your circuit would become a precarious, tangled, and confusing mess of wires hanging in mid-air or lying on the bench.

A more robust and orderly way to construct a circuit is to build it on a breadboard. The breadboard contains sockets for inserting components and wires.

This allows you to build circuits without having to spend time soldering components together and, hence, also allows flexibility in changing and moving components and wire connections.

There are some basic guidelines for building circuits on a breadboard and handling components.

These rules were developed over time to help you troubleshoot your circuits quickly, you work faster and more efficiently, and avoid common problems and incorrect connections that may lead to equipment damage.

As you build your circuit, aim for: 1. Verify value of components resistors, capacitors, etc. They may have been placed in the wrong bins.

Organize your components before building your circuit. Create a sheet with labels for identifying and arranging components, as is shown in Figure You will be able to build your circuit a lot faster if you have your components identified and laid out ahead of time.

Build your circuit to resemble the circuit diagram. This makes it a lot easier to trace through your circuit and troubleshoot it. Mark off the components on the diagram as you insert them into your circuit.

Breadboarding Basics 81 Figure Keep your work neat. You will often have to locate a particular point or component in the circuit, to make a measurement or try a component of a different value.

Neat circuits are also easier to troubleshoot. Use a color-coding scheme for wire connections. Wires come in different colors and using a color-coding scheme helps you to easily trace through your circuit when troubleshooting.

This helps your TA to help you troubleshoot. Your TA reserves the right NOT to help you troubleshoot your circuit if you do not follow this color scheme.

Beyond that, the choice is yours. For example, you could choose to identify all outputs with yellow wire.

That way, you can find and check all of your output voltages with ease. Create power and ground busses at the top and bottom of your breadboard.

Frequently, you will need to provide power to more than one place in your circuit. Using the top and bottom rows of the breadboard as power Section Breadboarding Basics 82 Figure Note that the busses have a break in the very middle!

If you want a power or ground bus to run the length of the breadboard, you must insert a jumper in the middle of the row to join the two halves together.

This provides a convenient place for clipping alligator clips, especially useful for grounding and power signals.

Keep your component leads short. Component leads are not insulated. Long leads also get messy and make it hard to check your circuit.

Never bend a lead at the body of the component. If you bend a component lead right at the component body, you risk damaging it or even tearing the lead off.

Bend the leads a small distance away from the component body. Route wires around IC chips instead of over them.

Never force large wires or components into the breadboard socket contacts. Numbers higher than 24 indicate wire that may be too thin to provide reliable connections, while numbers lower than 22 indicate wire that is thick enough to damage the breadboard socket contacts.

In Figure On the other hand, in Figure Breadboarding Basics 83 wires are routed around IC chips, power busses are established, and pigtails have been used at binding posts.

Breadboarding Basics a Poor breadboard wiring. Following are explanations of the independent, series, and parallel tracking modes. There are no internal connections between the two variable supplies.

Each variable supply can be set to deliver a different voltage and current. Using external connections, you can operate the power supply in three different independent modes.

These are shown in Figure Floating mode The power supply is not referenced with respect to earth ground the green terminal labeled by the rake symbol.

Each variable supply basically acts like a battery. Ground-referenced mode One of the output terminals is grounded to the earth using the green terminal, providing a fixed reference point for your measurement.

In this course, the green terminal and ground-referenced mode is NOT used. Stacked mode The negative output terminal of one variable power supply is connected to the positive output terminal of the other.

A stacked configuration can be either floating or ground-referenced. Series: In series mode, the positive output terminal of the master variable power supply is internally connected to the negative output terminal of the slave power supply.

This connection allows the power supply to produce a maximum voltage difference of 60 V i. When you place the power supply in series mode, the output terminals are hooked together internally as shown in Figure The voltage knob for the Section Tektronix PS DC Power Supply 87 master variable power supply controls the voltage for both variable power supplies.

Using the master voltage control, the slave supply voltage is automatically tracked to the same value as the master supply.

Parallel: In parallel tracking mode, the positive output terminals of both variable power supplies are internally connected, and the negative output terminals of both variable power supplies are internally connected.

These connections allow the power supply to produce a maximum voltage difference of 30 V at 0 to 4 A. When you place the power supply in parallel mode, the output terminals are internally connected as shown in Figure That is, it draws current from your signal.

This resistive loading, or signal current draw, changes the operation of your circuitry. To illustrate this, consider the measurement of a simple DC voltage divider circuit shown in Figure Before a probe is attached, the voltage is divided across the resistors.

Our edge mask is mostly a pure white background value of with some black edges value of 0 and the dots of noise also values of 0.

We could use a standard closing morphological operator, but it will remove a lot of edges. So, instead, we will apply a custom filter that removes small black regions that are surrounded completely by white pixels.

This will remove a lot of noise while having little effect on actual edges. We will scan the image for black pixels, and at each black pixel we'll check the border of the 5 x 5 square around it to see if all the 5 x 5 border pixels are white.

If they are all white we know we have a small island of black noise, so we fill the whole block with white pixels to remove the black island.

For simplicity in our 5 x 5 filter, we will ignore the two border pixels around the image and leave them as they are. Replace the package name at the top of FpsMeter.

In the file CartoonifierViewBase. Draw the FPS onto the screen for each frame, in run after the canvas.

The sample code that the Cartoonifier is based on uses the closest camera preview resolution to the screen height.

So if your device has a 5 megapixel camera and the screen is just x , it might use a camera resolution of x , and so on.

If you want to control which camera resolution is chosen, you can modify the parameters to setupCamera in the surfaceChanged function in CartoonifierViewBase.

Or if you want it to run really fast, pass 1 x 1 and it will find the lowest camera preview resolution for example x for you. Customizing the app Now that you have created a whole Android Cartoonifier app, you should know the basics of how it works and which parts do what; you should customize it!

Change the GUI, the app behavior and workflow, the cartoonifier filter constants, the skin detector algorithm, or replace the cartoonifier code with your own ideas.

Beware that face detection may take many seconds on some devices or high-resolution cameras, so this approach may be limited by the comparatively slow processing speed, but smartphones and tablets are getting significantly faster every year, so this will become less of a problem.

The most significant way to speed up mobile computer vision apps is to reduce the camera resolution as much as possible for example, 0.

Remember, there may be an optimized version of OpenCV for your device. To make customizing NDK and desktop image-processing code easier, this book comes with files ImageUtils.

It includes functions such as printMatInfo , which prints a lot of information about a cv::Mat object, making debugging OpenCV much easier.

This is useful when your OpenCV code is not working as expected; particularly for mobile development where it is often quite difficult to use an IDE debugger, and printf statements generally won't work in Android NDK.

However, the functions in ImageUtils work on both Android and desktop. Summary This chapter has shown several different types of image-processing filters that can be used to generate various cartoon effects: a plain sketch mode that looks like a pencil drawing, a paint mode that looks like a color painting, and a cartoon mode that overlays the sketch mode on top of the paint mode to make the image appear like a cartoon.

It also shows that other fun effects can be obtained, such as the evil mode that greatly enhances noisy edges, and the alien mode that changes the skin of the face to appear bright green.

There are many commercial smartphone apps that perform similar fun effects on the user's face, such as cartoon filters and skin-color changers.

There are also professional tools using similar concepts, such as skin-smoothing video post-processing tools that attempt to beautify women's faces by smoothing their skin while keeping the edges and non-skin regions sharp, in order to make their faces appear younger.

This chapter shows how to port the app from a desktop application to an Android mobile app, by following the recommended guidelines of developing a working desktop version first, porting it to a mobile app, and creating a user interface that is suitable for the mobile app.

The image-processing code is shared between the two projects so that the reader can modify the cartoon filters for the desktop application, and by rebuilding the Android app it should automatically show their modifications in the Android app as well.

It is expected that the reader can add the same functionality to an equivalent project in future versions of OpenCV4Android.

This book includes source code for both the desktop project and the Android project. As a result, the technology functions by enhancing one's current perception of reality.

Augmentation is conventionally in real-time and in semantic context with environmental elements. With the help of advanced AR technology for example, adding computer vision and object recognition the information about the surrounding real world of the user becomes interactive and can be digitally manipulated.

Artificial information about the environment and its objects can be overlaid on the real world. Starting from scratch, we will create an application that uses markers to draw some artificial objects on the images acquired from the camera.

Also, aspects such as capturing a video from a built-in camera, 3D scene rendering using OpenGL ES, and building of a common AR application architecture are going to be explained.

This is the only way to build apps for this platform. To run your applications on the device, you will have to purchase the Apple Developer Certificate for USD 99 per year.

It's impossible to run developed applications on the device without this certificate. We will assume readers have some experience using this IDE.

However, all complex parts of application source code will be explained in detail. From this chapter you'll learn more about markers. The full detection routine is explained.

After reading this chapter you will be able to write your own marker detection algorithm, estimate the marker pose in 3D world with regards to camera pose, and use this transformation between them to visualize arbitrary 3D objects.

You'll find the example project in this book's media for this chapter. It's a good starting point to create your first mobile Augmented Reality application.

This example will show you how to get access to the raw video data stream from the device camera, perform image processing using the OpenCV library, find a marker in an image, and render an AR overlay.

This step is necessary because in this application we will use a lot of functions from this library to detect markers and estimate position position.

OpenCV is a library of programming functions for real-time computer vision. It was originally developed by Intel and is now supported by Willow Garage and Itseez.

It also has an official Python binding and unofficial bindings to Java and. NET languages. Starting from version 2. Don't worry if you are new to iOS development; a framework is like a bundle of files.

Usually each framework package contains a list of header files and list of statically linked libraries. Application frameworks provide an easy way to distribute precompiled libraries to developers.

OpenCV documentation explains this process in detail. For simplicity, we follow the recommended way and use the framework for this chapter.

After downloading the file we extract its content to the project folder, as shown in the following screenshot: To inform the XCode IDE to use any framework during the build stage, click on Project options and locate the Build phases tab.

From there we can add or remove the list of frameworks involved in the build process. Click on the plus sign to add a new framework, as shown in the following screenshot: [ 50 ] Chapter 2 From here we can choose from a list of standard frameworks.

But to add a custom framework we should click on the Add other button. The open file dialog box will appear.

Point it to opencv2. The precompiled headers are a great feature to speed up compilation time. Find a. The following code shows how to modify the.

That's all. Our project template is configured and we are ready to move further. Free advice: make a copy of this project; this will save you time when you are creating your next one!

Application architecture Each iOS application contains at least one instance of the UIViewController interface that handles all view events and manages the application's business logic.

This class provides the fundamental view-management model for all iOS apps. A view controller manages a set of views that make up a portion of your app's user interface.

The application that we are going to write will have only one view; that's why we choose a Single-View Application template to create one.

This view will be used to present the rendered picture. This means that the video source should be capable of choosing a camera device front- or back-facing camera , adjusting its parameters such as resolution of the captured video, white balance, and shutter speed , and grabbing frames without freezing the main UI.

The image processing routine will be encapsulated in the MarkerDetector class. This class provides a very thin interface to user code.

Usually it's a set of functions like processFrame and getResult. Actually that's all that ViewController should know about.

We must not expose low-level data structures and algorithms to the view layer without strong necessity. VisualizationController contains all logic concerned with visualization of the Augmented Reality on our view.

VisualizationController is also a facade that hides a particular implementation of the rendering engine.

Low code coherence gives us freedom to change these components without the need to rewrite the rest of your code.

Such an approach gives you the freedom to use independent modules on other platforms and compilers as well. For example, you can use the MarkerDetector class easily to develop desktop applications on Mac, Windows, and Linux systems without any changes to the code.

Likewise, you can decide to port VisualizationController on the Windows platform and use Direct3D for rendering. In this case you should write only new VisualizationController implementation; other code parts will remain the same.

This triggers video source to inform the user code about this event with a callback. ViewController handles this callback and performs the following operations: 1.

Sends a new frame to the visualization controller. Performs processing of the new frame using our pipeline. Sends the detected markers to the visualization stage.

Renders a scene. Let's examine this routine in detail. The rendering of an AR scene includes the drawing of a background image that has a content of the last received frame; artificial 3D objects are drawn later on.

When we send a new frame for visualization, we are copying image data to internal buffers of the rendering engine.

This is not actual rendering yet; we are just updating the text with a new bitmap. The second step is the processing of new frame and marker detection.

We pass our image as input and as a result receive a list of the markers detected. These markers are passed to the visualization controller, which knows how to deal with them.

Let's take a look at the following sequence diagram where this routine is shown: [ 54 ] Chapter 2 We start development by writing a video capture component.

This class will be responsible for all frame grabbing and for sending notifications of captured frames via user callback.

Later on we will write a marker detection algorithm. This detection routine is the core of your application.

In this part of our program we will use a lot of OpenCV functions to process images, detect contours on them, find marker rectangles, and estimate their position.

After that we will concentrate on visualization of our results using Augmented Reality. After bringing all these things together we will complete our first AR application.

So let's move on! Accessing the camera The Augmented Reality application is impossible to create without two major things: video capturing and AR visualization.

The video capture stage consists of receiving frames from the device camera, performing necessary color conversion, and sending it to the processing pipeline.

As the single frame processing time is so critical to AR applications, the capture process should be as efficient as possible.

The best way to achieve maximum performance is to have direct access to the frames received from the camera. This became possible starting from iOS Version 4.

Existing APIs from the AVFoundation framework provide the necessary functionality to read directly from image buffers in memory.

This technique was used for iOS Version 3 and earlier. To get a bitmap, you have to create an intermediate instance of UIImage, copy an image to it, and get it back.

For AR applications this price is too high, because each millisecond matters. Losing a few frames per second FPS significantly decreases overall user experience.

Referring to Apple guidelines, you should avoid non-opaque layers because their blending is hard for mobile processors. Also you can set up the desired resolution of output frames.

However, it does affect overall performance since the larger the frame the more processing time and memory is required.

But first, let's take a look at the following figure where the capturing process for iOS is shown: AVCaptureSession is a root capture object that we should create.

The input device can either be a physical device camera or a video file not shown in diagram. In our case it's a built-in camera front or back.

The iOS platform is built on top of the Objective-C programming language. Create an instance of AVCaptureSession and set the capture session quality preset.

Choose and create AVCaptureDevice. You can choose the front- or backfacing camera or use the default one. Create an instance of AVCaptureVideoDataOutput and initialize it with format of video frame, callback delegate, and dispatch the queue.

Add the capture output to the capture session object. Start the capture session. Let's explain some of these steps in more detail.

After creating the capture session, we can specify the desired quality preset to ensure that we will obtain optimal performance.

Take care of return values for such an important thing as working with hardware setup is a good practice.

Without this, your code can crash in unexpected cases without informing the user what has happened.

We created a capture session and added a source of the video frames. For our purposes the BGRA color model fits best of all, as we will use this frame for visualization and image processing.

When started, it will capture frames from the camera and send it to user code. Then we lock it to prevent modifications by new frames.

Now we have exclusive access to the frame data. With help of the CoreVideo API, we get the image dimensions, stride number of pixels per row , and the pointer to the beginning of the image data.

Until we hold a lock on the pixel buffer, it guarantees consistency and correctness of its data.

Reading of pixels is available only after you have obtained a lock. When you're done, don't forget to unlock it to allow the OS to fill it with new data.

Marker detection A marker is usually designed as a rectangle image holding black and white areas inside it. Due to known limitations, the marker detection procedure is a simple one.

First of all we need to find closed contours on the input image and unwarp the image inside it to a rectangle and then check this against our marker model.

In this sample the 5 x 5 marker will be used. A source image taken from an iPad camera will be used as an example: Marker identification Here is the workflow of the marker detection routine: 1.

Convert the input image to grayscale. Perform binary threshold operation. Detect contours. Search for possible markers.

Detect and decode markers. Estimate marker 3D pose. Grayscale conversion The conversion to grayscale is necessary because markers usually contain only black and white blocks and it's much easier to operate with them on grayscale images.

Fortunately, OpenCV color conversion is simple enough. All further steps will be performed with the grayscale image.

Image binarization The binarization operation will transform each pixel of our image to black zero intensity or white full intensity.

This step is required to find contours. There are several threshold methods; each has strong and weak sides. The easiest and fastest method is absolute threshold.

In this method the resulting value depends on current pixel intensity and some threshold value.

If pixel intensity is greater than the threshold value, the result will be white ; otherwise it will be black 0.

The more preferable method is the adaptive threshold. The major difference of this method is the use of all pixels in given radius around the examined pixel.

Using average intensity gives good results and secures more robust corner detection. So the best way to locate a marker is to find closed contours and approximate them with polygons of 4 vertices.

The function skips contours that have their perimeter in pixels value set to be less than the value of the minContourPointsAllowed variable.

This is because we are not interested in small contours. They will probably contain no marker, or the contour won't be able to be detected due to a small marker size.

This is done to decrease the number of points that describe the contour shape. It's a good quality check to filter out areas without markers because they can always be represented with a polygon that contains four vertices.

If the approximated polygon has more than or fewer than 4 vertices, it's definitely not what we are looking for. To verify whether they are markers or not, we need to perform three steps: 1.

First, we should remove the perspective projection so as to obtain a frontal view of the rectangle area. Then we perform thresholding of the image using the Otsu algorithm.

This algorithm assumes a bimodal distribution and finds the threshold value that maximizes the extra-class variance while keeping a low intra-class variance.

Finally we perform identification of the marker code. If it is a marker, it has an internal code.

The marker is divided into a 7 x 7 grid, of which the internal 5 x 5 cells contain ID information. The rest correspond to the external black border.

Here, we first check whether the external black border is present. Then we read the internal 5 x 5 cells and check if they provide a valid code.

It might be required to rotate the code to get the valid one. This matrix can be calculated with the help of the cv::getPerspectiveTransform function.

It finds the perspective transformation from four pairs of corresponding points. The first argument is the marker coordinates in image space and the second point corresponds to the coordinates of the square marker image.

Then we try to extract the bit mask with the marker code. The codification employed is a slight modification of the hamming code.

In total, each word has only 2 bits of information out of the 5 bits employed. The other 3 are employed for error detection. As a consequence, we can have up to different IDs.

The main difference with the hamming code is that the first bit parity of bits 3 and 5 is inverted.

So, ID 0 which in hamming code is becomes in our code. The idea is to prevent a completely black rectangle from being a valid marker ID, with the goal of reducing the likelihood of false positives with objects of the environment.

Counting the number of black and white pixels for each cell gives us a 5 x 5-bit mask with marker code. To count the number of non-zero pixels on a certain image, the cv::countNonZero function is used.

This function counts non-zero array elements from a given 1D or 2D array. The same marker can have four possible representations depending on the camera's point of view: As there are four possible orientations of the marker picture, we have to find the correct marker position.

Remember, we introduced three parity bits for each two bits of information. With their help we can find the hamming distance for each possible marker orientation.

The correct marker position will have zero hamming distance error, while the other rotations won't. This error should be zero for correct marker ID; if it's not, it means that we encountered a wrong marker pattern corrupted image or false-positive marker detection.

This operation will help us in the next step when we will estimate the marker position in 3D.

We copy the list of vertices to the input array. Then we call cv::cornerSubPix, passing the actual image, list of points, and set of parameters that affect quality and performance of location refinement.

When done, we copy the refined locations back to marker corners as shown in the following image.

We do not use cornerSubPix in the earlier stages of marker detection due to its complexity. It's very expensive to call this function for large numbers of points in terms of computation time.

Therefore we do this only for valid markers. To place a 3D model in a scene, we need to know its pose with regard to a camera that we use to obtain the video frames.

We will use a Euclidian transformation in the Cartesian coordinate system to represent such a pose. In the next section you will learn how to obtain the A matrix and M vector parameters and calculate the [R T] transformation.

Camera calibration Each camera lens has unique parameters, such as focal length, principal point, and lens distortion model.

The process of finding intrinsic camera parameters is called camera calibration. The camera calibration process is important for Augmented Reality applications because it describes the perspective transformation and lens distortion on an output image.

To achieve the best user experience with Augmented Reality, visualization of an augmented object should be done using the same perspective projection.

To calibrate the camera, we need a special pattern image chessboard plate or black circles on white background. The camera that is being calibrated takes shots of this pattern from different points of view.

For this sample we provide internal parameters for all modern iOS devices iPad 2, iPad 3, and iPhone 4. Marker pose estimation With the precise location of marker corners, we can estimate a transformation between our camera and a marker in 3D space.

This operation is known as pose estimation from 2D-3D correspondences. The pose estimation process finds a Euclidean transformation that consists only of rotation and translation components between the camera and the object.

Let's take a look at the following figure: [ 78 ] Chapter 2 The C is used to denote the camera center. The P1-P4 points are 3D points in the world coordinate system and the p1-p4 points are their projections on the camera's image plane.

Our goal is to find relative transformation between a known marker position in the 3D world p1-p4 and the camera C using an intrinsic matrix and known point projections on image plane P1-P4.

But where do we get the coordinates of marker position in 3D space? We imagine them. As our marker always has a square form and all vertices lie in one plane, we can define their corners as follows: We put our marker in the XY plane Z component is zero and the marker center corresponds to the 0.

It's a great hint, because in this case the beginning of our coordinate system will be in the center of the marker Z axis is perpendicular to the marker plane.

Here we pass the list of marker coordinates in 3D space a vector of four points. Here we pass the list of found marker corners.

If it is NULL, all of the distortion coefficients are set to 0. The function calculates the camera transformation in such a way that it minimizes reprojection error, that is, the sum of squared distances between the observed projection's imagePoints and the projected objectPoints.

The estimated transformation is defined by rotation rvec and translation components tvec. This is also known as Euclidean transformation or rigid transformation.

To obtain a 3 x 3 rotation matrix from the rotation vector, the function cv::Rodrigues is used. This function converts a rotation represented by a rotation vector and returns its equivalent rotation matrix.

Because cv::solvePnP finds the camera position with regards to marker pose in 3D space, we have to invert the found transformation.

The resulting transformation will describe a marker transformation in the camera coordinate system, which is much friendlier for the rendering process.

It's time to draw something. As already mentioned, to render the scene we will use OpenGL functions. OpenGL provides all the basic features for creating high-quality rendering.

There are a large number of commercial and open source 3D-engines Unity, Unreal Engine, Ogre, and so on. For this reason, OpenGL is the first and last candidate for building cross-platform rendering systems.

Understanding the principles of the rendering system will give you the necessary experience and knowledge to use these engines in the future or to write your own.

Creating the OpenGL rendering layer In order to use OpenGL functions in your application you should obtain an iOS graphics context surface, which will present the rendered scene to the user.

This context is usually bound to View, which the user sees. When it's unarchived it's sent -initWithCoder:. This is done on purpose.

The separation of responsibilities allows us to change the logic of the visualization later. It performs the following steps: 1.

Clears the scene. Sets up orthographic projection for drawing the background. Draws the latest received image from the camera on a viewport.

Sets up perspective projection with regards to a camera's intrinsic parameters. For each detected marker, it moves the coordinate system to marker position in 3D.

It puts 4 x 4-transformation matrix to the OpenGl model-view matrix. Renders an arbitrary 3D object. Shows the frame buffer. The drawFrame function is called when the frame is ready to be drawn.

It happens when a new camera frame has been uploaded to video memory and the marker detection stage has been completed.

First of all we have to adjust the OpenGL projection matrix with regards to the camera intrinsic calibration matrix. Without this step we will have the wrong perspective projection.

Wrong perspective makes artificial objects look unnatural, as if they are "flying in the air" and not a part of the real world.

Correct perspective is a must-have for any Augmented Reality application. Each transformation can be presented as a 4 x 4 matrix and loaded to the OpenGL model view matrix.

This will move the coordinate system to the marker position in the world coordinate system. For example, let's draw a coordinate axis on the top of each marker that will show its orientation in space, and a rectangle with gradient fill that overlays the whole marker.

This visualization will give us visual feedback that our code is working as expected. You gained knowledge on how to use the OpenCV library within the XCode projects to create stunning state-of-the-art applications.

Usage of OpenCV enables your application to perform complex image processing computations on mobile devices with real-time performance. From this chapter you also learned how to perform the initial image processing translation in shades of gray and binarization , how to find closed contours in the image and approximate them with polygons, how to find markers in the image and decode them, how to compute the marker position in space, and the visualization of 3D objects in Augmented Reality.

Hartley and A. Zisserman, Cambridge University Press, ISBN [ 92 ] Marker-less Augmented Reality In this chapter readers will learn how to create a standard real-time project using OpenCV for desktop , and how to perform a new method of marker-less augmented reality, using the actual environment as the input instead of printed square markers.

This chapter will cover some of the theory of marker-less AR and show how to apply it in useful projects. CMake is a cross-platform, open-source build system designed to build, test, and package software.

Like the OpenCV library, the demonstration project for this chapter also uses the CMake build system. However, all complex parts of the application source code will be explained in detail.

Marker-less Augmented Reality Marker-based versus marker-less AR From the previous chapter you've learned how to use special images called markers to augment a real scene.

Marker-less AR is a technique that is based on recognition of objects that exist in the real world. A few examples of a target for marker-less AR are: magazine covers, company logos, toys, and so on.

In general, any object that has enough descriptive and discriminative information regarding the rest of the scene can be a target for marker-less AR.

The heart of the marker-less AR are image recognition and object detection algorithms. Unlike markers, whose shape and internal structure is fixed and known, real objects cannot be defined in such a way.

Also, objects can have a complex shape and require modified pose estimation algorithms to find their correct 3D transformations.

To give you an idea of marker-less AR, we will use a planar image as a target. Objects with complex shapes will not be considered here in detail.

We will discuss the use of complex shapes for AR later in this chapter. In this chapter, we will be targeting desktop platforms such as PC or Mac.

For this purpose, we need a cross-platform build system. In this chapter we use the CMake build system. Using feature descriptors to find an arbitrary image on video Image recognition is a computer vision technique that searches the input image for a particular bitmap pattern.

Our image recognition algorithm should be able to detect the pattern even if it is scaled, rotated, or has different brightness than of the original image.

How do we compare the pattern image against other images?

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Free quotes home career today. Quantitatively compare your experimental values with your calculated values and account for error sources as before.

Was the agreement above just a coincidence? The Superposition Principle [LR 4] says one can simply add up the voltage or current contributions from each individual independent source to get total current or voltage.

Take a look at the corresponding resistor currents and voltages from each set of measurements. Do they contribute to the total as expected?

How close are they? Account for sources of error. What new error source might you need to consider? DC Circuit Analysis 25 4. Does this rule work for AC voltages as well?

Verify these values experimentally, using your function generator to generate the AC voltage, V2. Hint: The oscilloscope and its subtract function may be of immense assistance in these measurements.

Be mindful to only have ONE earth ground the function generator ground is always earth ground and the oscilloscope ground is always earth ground too.

Did these results turn out as expected? What uncertainty do the function generator and oscilloscope introduce? This laboratory will give you a deeper understanding of capacitors; a linear circuit element that, unlike a resistor, can store energy.

You will determine the equivalent capacitance of capacitors connected in parallel and capacitors connected in series. Using this, you will be able to simplify complicated capacitor networks.

You will also see the relation between the current through a capacitor and the voltage applied. With this, you will gain a better feeling for the properties of capacitors.

We say this circuit element is linear if the function f is a linear function. The function f is said to be linear if it satisfies the following two properties: 1.

Also, if we multiply the input by a constant, then the output is multiplied by the same constant.

Capacitors: A capacitor is a passive linear circuit element designed to store energy. A capacitor consists of two conducting plates separated by an insulator.

We then say that the capacitor is storing the charge q. By differentiating equation 4. Show that a capacitor, whose current-voltage relationship is given by equation 4.

Looking at the circuit in Figure 4. For the same DC power supply, calculate the energy stored in each capacitor.

First, calculate the equivalent capacitance CAB across terminals A and B that is, the equivalent capacitance of the loop containing the 3.

Finally, calculate the equivalent capacitance of Ceq of the entire circuit. To calculate these voltages you will have to make use of Ceq and equation 4.

Capacitors: One: 4. Section 1: Capacitor Voltage and Current 1. Build the circuit shown in Figure 4. Remember to color-code your circuit!

Measure the voltage across each capacitor. How do the measured voltages [LR 1] agree with the values you calculated in the pre-lab?

Try to determine the factors that could contribute to this discrepancy. If you can, quantify the contributions. Measure the current through each capacitor.

Referring to equation 4. Explain why or why not. Make sure to include units and label axes. Using equation 4.

Label the axes of your sketch. Section 2: Equivalent Capacitance Laboratory 4. Components such as capacitors are built to be within a certain tolerance of [LR 5] their stated values.

Measure the capacitance of each capacitor required for building the circuit in Figure 4. Record both the measured values and the stated values.

Now, use these capacitors to build the circuit in Figure 2. Compare these measurements to the values you calculated in the pre-lab.

Are the differences solely due to the capacitor tolerances? What else could be contributing to the difference in results?

What do you observe? Does this agree with your expectations from the pre-lab? Try to explain what is going on. Hint: Consider the effect of the meter.

In this laboratory you will gain experience with one of the most useful and intuitive electronic devices, a basic op-amp. In addition to many other functions, op-amps can be used to make stable voltage sources, signal filters or to perform mathematical operations like addition, subtraction, multiplication, division, differentiation and integration.

These are key components in electronic control systems. Any mechanical engineer interested in mechatronics will soon find op-amps indispensable.

This is a subtle feature but when contrasted against the new type of circuits in this lab, you will notice the difference.

The circuits of this lab control their output based on their input and output! That may sound strange at first, but you will soon appreciate this powerful idea known as feedback.

This is a fundamental idea in electronics and control systems. Positive feedback adds to the input. Negative feedback subtracts from the input.

Though positive feedback systems are inherently unstable think of what happens when a microphone collects sound from an amplifying speaker , negative feedback systems can offer a variety of benefits.

Negative feedback will increase stability and frequency response of an amplifier and permit careful control of amplifier gain despite device parameters and external effects like changing temperature.

Operational amplifiers typically take advantage of this effect. Without feedback, an operational amplifier will exhibit tremendous gain, known as open-loop gain i.

By using a negative feedback loop, you can subtract from this gain to achieve a desired final gain. This modified gain is known as the closed-loop gain.

Figure 5. You may notice that the two resistors at the output comprise a voltage divider. That is, the ratio of the two determine what fraction of Vout is between the 30 Laboratory 5.

A wire connects this point to the negative terminal of the op-amp allowing it to sample a fraction of the output voltage at the input.

This is negative feedback! These two rules are possible under the ideal op-amp assumption. An ideal op-amp is shown in Figure 5. This shows what is going on inside the op-amp.

Due to the infinite input impendence, infinitesimal current, IIN enters at the input terminals. Since infinitesimal current enters the input, the offset voltage, ZOS , is infinitesimal.

Finally, the dependent voltage source amplifies this infinitesimal voltage by an infinite gain, a.

It is simply a more general form of resistance which accounts for timedependent resistances. Laboratory 5.

Dealing with indeterminate products products of zero and infinity is tricky, however, this model will help you to understand real, non-ideal op-amps.

Using information from the background section, formulate an expression for the closed-loop gain of the inverting amplifier in Figure 5.

You may have noticed that these amplifiers are active devices and thus, require a supply voltage to operate. This is possible because batteries act as floating voltage sources.

The DC power supply in the lab can operate this way. Keep this in mind when it is time to assemble your circuits.

Assemble the circuit shown in Figure 5. Also, make sure that these share the same ground as your input signal. To help with the C pin-outs, check the Laboratory 5.

If you suspect that your C is misbehaving, test-circuit that should be found at the front of the classroom to determine if the device is faulty.

Include a picture of your circuit in your lab report. Since you know the input voltage, Vin , you can use your calculated gain to find the expected Vout.

Measure Vout and the [LR 3] feedback voltage. Now you can use these to compare your experimental results to your calculations.

Are there any differences? If not or if so, explain why you arrived at this result. What is [LR 4] the input impedance of your non-ideal, C-based non-inverting amplifier?

It may be helpful to consider the voltage divider rule and to recognize this configuration as a combination of a module of known resistance and a module of unknown resistance.

Op-Amp Circuits I 34 2. Include a picture of your [LR 5] circuit in your lab report. Quantify the uncertainty on these calculations too!

Measure Vout [LR 7] and the feedback voltage. In this case, the feedback voltage at Pin 2 is known as virtual ground.

Now you can use these values for comparing your experimental results to your calculations. Again, explain any differences or lack-there-of.

The following are additional questions for your report and do not necessarily require laboratory practice. However, you may find experimental verification very useful.

What advantage does this [LR 8] type of notation offer? Express your experimentally determined gains in decibels.

This type of arrangement is known as a voltage follower. Explain the function of this amplifier and what it might be useful for. In this laboratory, you learn about several different uses of operational amplifiers.

Complex circuits are often built in stages, which are then connected, or cascaded, together.

When cascading two stages together there may be loading effects. This occurs when the input impedance of one stage is too low, and the result is that the overall circuit does not behave as expected.

You will build a voltage follower buffer which has a very high input impedance and can be used as an intermediate stage to isolate one circuit from the other.

You will also build a function generator that can be used to produce square and a triangle waveforms. One: 2. By buffer we mean, a module which will isolate the stages such that a change in second stage does not influence the performance of the first.

Measure the output of the stage, vo1. Now you can build the second stage of our circuit, shown in Figure 6. Measure, vo2 the output of the second stage.

What happens if we combine your two stages? Connect vi2 to vo1 so that the [LR 2] two stages of the circuit are cascaded together to form a single circuit.

Now, measure vo2. Does this value equal that which you measured for the second stage of the circuit alone? What you are seeing is loading effects.

To prevent this loading from occurring we can build a voltage follower and [LR 3] use it to isolate the two stages of your circuit.

To do this, build the circuit shown in Figure 6. Measure vo1 and vo2 and explain how the voltage follower succeeds in eliminating or at least reducing the loading effects.

What have you just made and why might this be useful? Dismantle your circuit. Laboratory 6. Section 2: Function Generator In this section you will build a function generator capable of producing square and triangular waveforms.

The circuit shown in Figure 6. Assemble the circuit shown in Figure 6. In the pre-lab assignment you were asked to assign values for the five [LR 4] resistors of the function generator circuit.

If the lab does not have an exact match for your specified resistors you should use the next closest resistor value; be sure to record the actual values used.

Using the relationships developed in the pre-lab, what is the new expected frequency of your waveforms?

Connect your oscilloscope to the circuit such that the square wave is shown [LR 5] on channel 1 CH1 and the triangle wave on channel 2 CH2.

Provide a screen shot of both waveforms. Record the peak-to-peak voltage and frequency for both waveforms. Does this match the values you calculated previously?

Frequency Control A function generator of fixed amplitude and frequency is of limited use. In the pre-lab you should have found that the frequency of the triangle wave and hence square wave is dependent on the resistors R1 to R5.

You also should have found that the amplitude of the triangle wave is dependent on resistors R1 to R4 but independent of R5.

Thus, in order to achieve frequency control independent of amplitude control we can replace R5 with a variable resistor1.

Op-Amp Circuits II 39 on the oscilloscope. Does the response behave as expected? What happens when the resistance is increased?

What happens when the resistance is decreased? Section 3: Comparator A comparator is an example of a op-amp used without negative feedback; the absence of negative feedback causes the op-amp to have infinite gain and run at saturation.

The sign of the output depends on the comparison between an input voltage to a reference voltage, see Figure 6.

Since the output of the comparator is piecewise i. Note that in Figure 6. Describe how you could change the circuit in Figure 6.

Figure 6. The comparator runs at either positive or negative saturation depending on the values of Vin and Vref. Slew Rate For an ideal comparator, switching between positive and negative saturation or vise versa occurs instantaneously; real op-amps, however, require a finite amount of time to adjust.

Turn CH2 off. Adjust the variable resistor added in the last section until you are generating a square wave with a frequency of 1 kHz.

Take a screen shot and calculate the slew rate, SR, for the LM chip. Make two sketches to demonstrate the affect of the slew rate on the square wave output.

One sketch should be of a low-frequency square wave and the other of a highfrequency square wave.

For this lab we are forcing a multipurpose op-amp to act as a comparator. For these comparators, the switching speed is on the order of nano-seconds, enabling quicker and more accurate switching.

Label each highlighted stage e. Briefly describe the purpose, or function, of each stage. The circuit makes use of two variable resistors. Describe the effect each variable resistors has on VGEN out.

Label and describe the purpose of each of the five highlighted stages of the function generator circuit shown. As mechanical engineers, you have previously learned to build first- and second-order dynamical systems with masses, springs and dashpots.

It turns out we can build electrical circuits out of resistors, capacitors, and inductors that are governed by the same equations!

This lab will give you the chance to build and test first-order dynamical systems from this circuit elements. You will also examine the frequency response of these circuits.

What about the resistor? That is, at each instant in time, the voltage across the resistor and capacitor must add to equal the applied voltage at that instant.

Since current is the migration of charge, one would expect the voltage drop across the resistor to be highest during the early part of the discharge.

What if the applied voltage is changed continuously as in AC? The previously considered circuits will 42 Laboratory 7.

First-Order Circuits 43 exhibit a response based on their impedance. This requires a completely different treatment.

We have encountered three major circuit elements: resistors, capacitors and inductors. In DC, resistors have a resistance, R, capacitors have an infinite resistance and inductors have zero resistance.

This treatment is actually a special case of the more general AC treatment. In AC, resistance is generalized in a term known as impedance, which essentially has the same effect, with an added twist.

Impedance, Z, is a complex number. That is, impedance is not restricted to the set of real numbers.

It may include imaginary numbers as well. In fact, in AC, the impedances of capacitors and inductors are pure imaginary numbers.

The impedance of a resistor is simply its resistance, which is a pure real number. As such, the equivalent impedance of the series capacitor and resistor in Figure 7.

So if the impedance of the resistor is simply the resistance, what is the impedance of capacitors and inductors? These elements are a little more interesting.

As you can see, the impedance of a capacitor will increase towards infinity at low frequency but the impedance of an inductor will decrease to zero.

How exciting! This is why we treated capacitors as open circuits and inductors as short circuits in DC. Equation 7. This is a very important distinction.

In terms of time-dependence, define transient and steady-state and indicate those terms in this equation. Put these tools to the test.

Assume voltage Vi in the RC circuit in Figure 7. Sketch the voltage across the capacitor and resistor as a function of time 3. Sketch the voltage across the capacitor and resistor as a function of time.

Look at Figure 7. Hint: This may seem very difficult at first, Laboratory 7. First-Order Circuits 44 but the same rules apply. This is still a step-response.

The only difference is your step does not start from 0 V. Solve for Vo in Figure 7. For the circuit in Figure 7.

What happens if we take Vo across the resistor instead of the capacitor? A low-pass filter attenuates high-frequencies while allowing low frequencies to pass with little attenuation.

The opposite applies to a high-pass filter. A band-pass filter allows a select band of frequencies to pass with little attenuation.

Indicate what type of filters you have in your sketches. Capacitors: One: 0. It would be interesting to observe this response in a circuit that you create and then compare theoretical predictions to experimental results.

Assemble the circuit shown in Figure 7. How might we implement a power supply for creating a sudden step in voltage? You may recall that the function generator can provide a square-wave output.

This is essentially a DC source which periodically changes its voltage. You can specify how often the voltage is changed by adjusting the frequency of the wave.

By adjusting the DC-offset and amplitude you can adjust what voltages the source switches between.

To make it simple, adjust your supply so Vi switches between 0 V and 2 V. Laboratory 7. But what about the period? Since it is of interest to see the whole response, [LR 1] that is, until steady state is reached, we should estimate how long to set the period.

Recall from the pre-lab where you identified the steady-state and transient portions of Vo t. When the transient portion goes to zero, we are at steady state.

Of course, you can see that this will take an infinite amount of time. Hook up your function generator to the circuit and observe the [LR 2] output, Vo t on the oscilloscope.

Capture your output and label the steady-state and transient portions. Did you notice that you are measuring the voltage across the capacitor?

In- [LR 3] dicate what the capacitor is doing at each portion of the curve on your screen capture charging, discharging or staying the same.

Can you verify your time constant experimentally? Since the time constant is often a critical design parameter, we should quantify the uncertainty in your calculations and measurements and indicate the sources oscilloscope, capacitors, etc.

In the pre-lab you sketched your prediction of the time-dependent voltage across the resistor. Can you vindicate your Laboratory 7.

Do you see any differences? If so, why might they exist? Capture this result, label the steady-state and transient portions, and describe what is happening.

You may want to check the components section of the manual to find out how the switch works. Build the circuit shown in Figure 7.

It will be helpful to view the response on the oscilloscope. Estimating the time constant may help you to easily capture the output.

When you are ready, flip the switch from position A to position B and observe the result. In the pre-lab, you generated a general formula for vo.

Do your these calculations match the corresponding measured values? You may want to include a screen-shot to augment your claim in your report.

Now we will apply continuously changing, AC voltages to the circuit in Figure 7. Set Vi as a sine wave with peak-to-peak amplitude 5 V and zero DC offset.

In the pre-lab, you showed that by taking the output across the capacitor [LR 7] or resistor of Figure 7.

Plot the data to show the trend and make sure you have enough points to do this sufficiently. It may be helpful to note that conventionally, these plots have logarithmic axes.

Keep this in mind when determining the range of frequencies to examine. The frequency at which half-power is obtained is known as the cutoff fre- [LR 8] quency.

This is an important number for any filter. Indicate the cutoff frequency on your plot. To better understand where the power is being dissipated, consider Figure 7.

It is across these resistors that the power is dissipated. Repeat the above two steps for the high-pass filter.

The cutoff frequencies [LR 10] you just found should correspond to a gain that is 3 dB less than the maximum.

Your plots will have characteristic shapes. Think about what these shapes [LR 11] mean and suggest a practical use for both types of circuits.

First-Order Circuits 48 2. In the previous laboratory we looked at circuits with a single storage element, a capacitor. In this laboratory we will investigate circuits which contain two storage elements.

Such circuits are known as secondorder circuits. We will look at a series RLC circuit which contains a resistor an inductor and a capacitor in series.

We will see how, by varying the values of these three components, we can obtain three distinct voltage responses; underdamped, critically damped, and overdamped.

For this laboratory we will derive the necessary background in the Pre-lab section. Call this current i.

Express the voltage across the resistor and the voltage across the inductor in terms of this current i.

The constant A should also depend on the input voltage V. Inductors: One: 0. One: 0. We will use the function generator to provide the voltage V to the circuit.

However, this circuit has a very low input impedance. To buffer the function generator from the RLC circuit, we will build a voltage follower.

Build the voltage follower shown in Figure 8. They filter out any AC component of the signal, and ensure that the voltage supplied to op-amp is steady.

Laboratory 8. Connect the function generator to vib and verify that the output at vob is as expected. Leave this circuit assembled.

Build the series RLC circuit shown in Figure 8. Use the output of the voltage follower, vob as V. Use both channels of the oscilloscope, displaying the square wave on one channel, and vC t on the other.

Adjust the frequency of the square wave so that the step response of the circuit is clearly visible. Is your system overdamped, underdamped, or critically damped?

Take a [LR 1] screenshot of the response. Second-Order Circuits 52 5. Calculate the resistance, Rcrit , required to make the system critically damped.

Compare this resistance with the value you calculated for Rcrit. Comment on any differences between the two values. Experiment with different values of R.

Notice how the step response changes [LR 4] as the resistance is varied. For what values of R is the system underdamped?

Take screenshots of these two responses. Now, switch the resistor and capacitor so that you can measure vR t , and [LR 5] set R so that the system is underdamped.

Take a screenshot of the response. Does this response make sense? Explain why or why not? All further steps will be performed with the grayscale image.

Image binarization The binarization operation will transform each pixel of our image to black zero intensity or white full intensity.

This step is required to find contours. There are several threshold methods; each has strong and weak sides. The easiest and fastest method is absolute threshold.

In this method the resulting value depends on current pixel intensity and some threshold value. If pixel intensity is greater than the threshold value, the result will be white ; otherwise it will be black 0.

The more preferable method is the adaptive threshold. The major difference of this method is the use of all pixels in given radius around the examined pixel.

Using average intensity gives good results and secures more robust corner detection. So the best way to locate a marker is to find closed contours and approximate them with polygons of 4 vertices.

The function skips contours that have their perimeter in pixels value set to be less than the value of the minContourPointsAllowed variable.

This is because we are not interested in small contours. They will probably contain no marker, or the contour won't be able to be detected due to a small marker size.

This is done to decrease the number of points that describe the contour shape. It's a good quality check to filter out areas without markers because they can always be represented with a polygon that contains four vertices.

If the approximated polygon has more than or fewer than 4 vertices, it's definitely not what we are looking for.

To verify whether they are markers or not, we need to perform three steps: 1. First, we should remove the perspective projection so as to obtain a frontal view of the rectangle area.

Then we perform thresholding of the image using the Otsu algorithm. This algorithm assumes a bimodal distribution and finds the threshold value that maximizes the extra-class variance while keeping a low intra-class variance.

Finally we perform identification of the marker code. If it is a marker, it has an internal code. The marker is divided into a 7 x 7 grid, of which the internal 5 x 5 cells contain ID information.

The rest correspond to the external black border. Here, we first check whether the external black border is present.

Then we read the internal 5 x 5 cells and check if they provide a valid code. It might be required to rotate the code to get the valid one.

This matrix can be calculated with the help of the cv::getPerspectiveTransform function. It finds the perspective transformation from four pairs of corresponding points.

The first argument is the marker coordinates in image space and the second point corresponds to the coordinates of the square marker image.

Then we try to extract the bit mask with the marker code. The codification employed is a slight modification of the hamming code.

In total, each word has only 2 bits of information out of the 5 bits employed. The other 3 are employed for error detection. As a consequence, we can have up to different IDs.

The main difference with the hamming code is that the first bit parity of bits 3 and 5 is inverted. So, ID 0 which in hamming code is becomes in our code.

The idea is to prevent a completely black rectangle from being a valid marker ID, with the goal of reducing the likelihood of false positives with objects of the environment.

Counting the number of black and white pixels for each cell gives us a 5 x 5-bit mask with marker code. To count the number of non-zero pixels on a certain image, the cv::countNonZero function is used.

This function counts non-zero array elements from a given 1D or 2D array. The same marker can have four possible representations depending on the camera's point of view: As there are four possible orientations of the marker picture, we have to find the correct marker position.

Remember, we introduced three parity bits for each two bits of information. With their help we can find the hamming distance for each possible marker orientation.

The correct marker position will have zero hamming distance error, while the other rotations won't. This error should be zero for correct marker ID; if it's not, it means that we encountered a wrong marker pattern corrupted image or false-positive marker detection.

This operation will help us in the next step when we will estimate the marker position in 3D. We copy the list of vertices to the input array.

Then we call cv::cornerSubPix, passing the actual image, list of points, and set of parameters that affect quality and performance of location refinement.

When done, we copy the refined locations back to marker corners as shown in the following image. We do not use cornerSubPix in the earlier stages of marker detection due to its complexity.

It's very expensive to call this function for large numbers of points in terms of computation time. Therefore we do this only for valid markers.

To place a 3D model in a scene, we need to know its pose with regard to a camera that we use to obtain the video frames. We will use a Euclidian transformation in the Cartesian coordinate system to represent such a pose.

In the next section you will learn how to obtain the A matrix and M vector parameters and calculate the [R T] transformation. Camera calibration Each camera lens has unique parameters, such as focal length, principal point, and lens distortion model.

The process of finding intrinsic camera parameters is called camera calibration. The camera calibration process is important for Augmented Reality applications because it describes the perspective transformation and lens distortion on an output image.

To achieve the best user experience with Augmented Reality, visualization of an augmented object should be done using the same perspective projection.

To calibrate the camera, we need a special pattern image chessboard plate or black circles on white background. The camera that is being calibrated takes shots of this pattern from different points of view.

For this sample we provide internal parameters for all modern iOS devices iPad 2, iPad 3, and iPhone 4. Marker pose estimation With the precise location of marker corners, we can estimate a transformation between our camera and a marker in 3D space.

This operation is known as pose estimation from 2D-3D correspondences. The pose estimation process finds a Euclidean transformation that consists only of rotation and translation components between the camera and the object.

Let's take a look at the following figure: [ 78 ] Chapter 2 The C is used to denote the camera center. The P1-P4 points are 3D points in the world coordinate system and the p1-p4 points are their projections on the camera's image plane.

Our goal is to find relative transformation between a known marker position in the 3D world p1-p4 and the camera C using an intrinsic matrix and known point projections on image plane P1-P4.

But where do we get the coordinates of marker position in 3D space? We imagine them. As our marker always has a square form and all vertices lie in one plane, we can define their corners as follows: We put our marker in the XY plane Z component is zero and the marker center corresponds to the 0.

It's a great hint, because in this case the beginning of our coordinate system will be in the center of the marker Z axis is perpendicular to the marker plane.

Here we pass the list of marker coordinates in 3D space a vector of four points. Here we pass the list of found marker corners.

If it is NULL, all of the distortion coefficients are set to 0. The function calculates the camera transformation in such a way that it minimizes reprojection error, that is, the sum of squared distances between the observed projection's imagePoints and the projected objectPoints.

The estimated transformation is defined by rotation rvec and translation components tvec. This is also known as Euclidean transformation or rigid transformation.

To obtain a 3 x 3 rotation matrix from the rotation vector, the function cv::Rodrigues is used. This function converts a rotation represented by a rotation vector and returns its equivalent rotation matrix.

Because cv::solvePnP finds the camera position with regards to marker pose in 3D space, we have to invert the found transformation.

The resulting transformation will describe a marker transformation in the camera coordinate system, which is much friendlier for the rendering process.

It's time to draw something. As already mentioned, to render the scene we will use OpenGL functions. OpenGL provides all the basic features for creating high-quality rendering.

There are a large number of commercial and open source 3D-engines Unity, Unreal Engine, Ogre, and so on. For this reason, OpenGL is the first and last candidate for building cross-platform rendering systems.

Understanding the principles of the rendering system will give you the necessary experience and knowledge to use these engines in the future or to write your own.

Creating the OpenGL rendering layer In order to use OpenGL functions in your application you should obtain an iOS graphics context surface, which will present the rendered scene to the user.

This context is usually bound to View, which the user sees. When it's unarchived it's sent -initWithCoder:. This is done on purpose.

The separation of responsibilities allows us to change the logic of the visualization later. It performs the following steps: 1. Clears the scene.

Sets up orthographic projection for drawing the background. Draws the latest received image from the camera on a viewport. Sets up perspective projection with regards to a camera's intrinsic parameters.

For each detected marker, it moves the coordinate system to marker position in 3D. It puts 4 x 4-transformation matrix to the OpenGl model-view matrix.

Renders an arbitrary 3D object. Shows the frame buffer. The drawFrame function is called when the frame is ready to be drawn.

It happens when a new camera frame has been uploaded to video memory and the marker detection stage has been completed.

First of all we have to adjust the OpenGL projection matrix with regards to the camera intrinsic calibration matrix. Without this step we will have the wrong perspective projection.

Wrong perspective makes artificial objects look unnatural, as if they are "flying in the air" and not a part of the real world.

Correct perspective is a must-have for any Augmented Reality application. Each transformation can be presented as a 4 x 4 matrix and loaded to the OpenGL model view matrix.

This will move the coordinate system to the marker position in the world coordinate system.

For example, let's draw a coordinate axis on the top of each marker that will show its orientation in space, and a rectangle with gradient fill that overlays the whole marker.

This visualization will give us visual feedback that our code is working as expected. You gained knowledge on how to use the OpenCV library within the XCode projects to create stunning state-of-the-art applications.

Usage of OpenCV enables your application to perform complex image processing computations on mobile devices with real-time performance.

From this chapter you also learned how to perform the initial image processing translation in shades of gray and binarization , how to find closed contours in the image and approximate them with polygons, how to find markers in the image and decode them, how to compute the marker position in space, and the visualization of 3D objects in Augmented Reality.

Hartley and A. Zisserman, Cambridge University Press, ISBN [ 92 ] Marker-less Augmented Reality In this chapter readers will learn how to create a standard real-time project using OpenCV for desktop , and how to perform a new method of marker-less augmented reality, using the actual environment as the input instead of printed square markers.

This chapter will cover some of the theory of marker-less AR and show how to apply it in useful projects. CMake is a cross-platform, open-source build system designed to build, test, and package software.

Like the OpenCV library, the demonstration project for this chapter also uses the CMake build system. However, all complex parts of the application source code will be explained in detail.

Marker-less Augmented Reality Marker-based versus marker-less AR From the previous chapter you've learned how to use special images called markers to augment a real scene.

Marker-less AR is a technique that is based on recognition of objects that exist in the real world. A few examples of a target for marker-less AR are: magazine covers, company logos, toys, and so on.

In general, any object that has enough descriptive and discriminative information regarding the rest of the scene can be a target for marker-less AR.

The heart of the marker-less AR are image recognition and object detection algorithms. Unlike markers, whose shape and internal structure is fixed and known, real objects cannot be defined in such a way.

Also, objects can have a complex shape and require modified pose estimation algorithms to find their correct 3D transformations.

To give you an idea of marker-less AR, we will use a planar image as a target. Objects with complex shapes will not be considered here in detail.

We will discuss the use of complex shapes for AR later in this chapter. In this chapter, we will be targeting desktop platforms such as PC or Mac.

For this purpose, we need a cross-platform build system. In this chapter we use the CMake build system.

Using feature descriptors to find an arbitrary image on video Image recognition is a computer vision technique that searches the input image for a particular bitmap pattern.

Our image recognition algorithm should be able to detect the pattern even if it is scaled, rotated, or has different brightness than of the original image.

How do we compare the pattern image against other images? As the pattern can be affected by perspective transformation, it's obvious that we can't directly compare pixels of the pattern and test image.

The feature points and feature descriptors are helpful in this case. There is no universal or exact definition of what the feature is.

The exact definition often depends on the problem or the type of application. Usually a feature is defined as an "interesting" part of an image, and features are used as a starting point for many computer vision algorithms.

In this chapter we will use a feature point term, which is a part of the image defined by a center point, radius, and orientation.

Each feature-detection algorithm tries to detect the same feature points regardless of the perspective transformation applied. Feature extraction Feature detection is the method of finding areas of interest from the input image.

There are a lot of feature-detection algorithms, which search for edges, corners, or blobs. In our case we are interested in corner detection.

The corner detection is based on an analysis of the edges in the image. A corner-based edge detection algorithm searches for rapid changes in the image gradient.

Usually it's done by looking for extremums of the first derivative of the image gradients in the X and Y directions. Feature-point orientation is usually computed as a direction of dominant image gradient in a particular area.

When the image is rotated or scaled, the orientation of dominant gradient is recomputed by the feature-detection algorithm. This means that regardless of image rotation, the orientation of feature points will not change.

Such features are called rotation invariant. Some of the feature-detection algorithms use fixed-size features, while others calculate the optimal size for each keypoint separately.

Knowing the feature size allows us to find the same feature points on scaled images. This makes features scale invariant.

OpenCV has several feature-detection algorithms. All of them are derived from the base class cv::FeatureDetector.

The explicit class creation allows you to pass additional arguments to the feature detector constructor, while the creation by algorithm name makes it easier to switch the algorithm during runtime.

Each keypoint contains its center, radius, angle, and score, and has some correlation with the "quality" or "strength" of the feature point.

Each feature-detection algorithm has its own score computation algorithm, so it's valid to compare scores of the keypoints detected by a particular detection algorithm.

Corner-based feature detectors use a grayscale image to find feature points. Descriptor-extraction algorithms also work with grayscale images.

Of course, both of them can do color conversion implicitly. But in this case the color conversion will be done twice. We can improve performance by doing an explicit color conversion of the input image to grayscale and use that for feature detection and descriptor extraction.

This makes keypoints invariant to rotation and scale. Unfortunately, they are patented; so they are not free for commercial use.

However, their implementation is present in OpenCV, so you can evaluate them freely. But there are good and free replacements available.

The original FAST detector is amazingly fast but does not calculate the orientation or the size of the keypoint.

Fortunately, the ORB algorithm does estimate keypoint orientation, but the feature size is still fixed. From the following paragraphs you will learn nice and cheap tricks of dealing with this.

But first, let me explain why the feature point matters so much in image recognition. If we deal with images, which usually have a color depth of 24 bits per pixel, for a resolution of x , we have KB of data.

How do we find our pattern image in the real world? Pixel-to-pixel matching takes too long and we will have to deal with rotation and scaling too.

It's definitely not an option. Using feature points can solve this problem. By detecting keypoints, we can be sure that returned features describe parts of the image that contains lot of information that's because corner-based detectors return edges, corners, and other sharp figures.

So to find correspondences between two frames, we only have to match keypoints. From the patch defined by the keypoint, we extract a vector called descriptor.

It's a form of representation of the feature point. There are many methods of extraction of the descriptor from the feature point.

All of them have their strengths and weaknesses. In our sample project we use the ORB descriptor-extraction algorithm because we choose it as a feature detector too.

It's always a good idea to use both feature detector and descriptor extractor from the same algorithm, as they will then fit each other perfectly.

Feature descriptor is represented as a vector of fixed size 16 or more elements. Let's say our image has a resolution of x pixels and it has 1, feature points.

It's ten times smaller than the original image data. Also, it's much easier to operate with descriptors rather than with raster bitmaps.

Usually its L2 norm or hamming distance based upon the kind of feature descriptor used. Likewise, as feature-detection algorithms they can be created by either specifying their name or with explicit constructor calls.

It's called the "matching" procedure. The first set of descriptors is usually called the train set because it corresponds to our pattern image.

The second set is called the query set as it belongs to the image where we will be looking for the pattern.

The more correct matches found more patterns to image correspondences exist the more chances are that the pattern is present on the image.

To increase the matching speed, you can train a matcher before by calling the match function. The training stage can be used to optimize the performance of cv::FlannBasedMatcher.

For this, the train class will build index trees for train descriptors. And this will increase the matching speed for large data sets for example, if you want to find a match from hundreds of images.

For cv::BFMatcher the train class does nothing as there is nothing to preprocess; it simply stores the train descriptors in the internal fields.

It's normal. But we can't deal with them because the matching algorithm has rejected them. Our goal is therefore to minimize the number of false-positive matches.

To reject wrong correspondences, we can use a cross-match technique. The idea is to match train descriptors with the query set and vice versa.

Only common matches for these two matches are returned. Such techniques usually produce best results with minimal number of outliers when there are enough matches.

Two nearest descriptors are returned for each match. The match is returned only if the distance ratio between the first and second matches is big enough the ratio threshold is usually near two.

But in some cases, false-positive matches can pass through this test. In the next section, we will show you how to remove the rest of outliers and leave only correct matches.

Homography estimation To improve our matching even more, we can perform outlier filtration using the random sample consensus RANSAC method.

As we're working with an image a planar object and we expect it to be rigid, it's ok to find the homography transformation between feature points on the pattern image and feature points on the query image.

Homography transformations will bring points from a pattern to the query image coordinate system. To find this transformation, we use the cv::findHomography function.

As a side effect, this function marks each correspondence as either inlier or outlier, depending on the reprojection error for the calculated homography matrix.

Homography refinement When we look for homography transformations, we already have all the necessary data to find their locations in 3D.

However, we can improve its position even more by finding more accurate pattern corners. For this we warp the input image using estimated homography to obtain a pattern that has been found.

The result should be very close to the source train image. Homography refinement can help to find more accurate homography transformations.

The resultant precise homography will be the matrix product of the first H1 and second H2 homography. It takes ownership on the feature detection and descriptor-extraction algorithm, feature matching logic, and settings that control the detection process.

Also, it trains a descriptor matcher with a pattern's descriptor set. After calling this method we are ready to find our train image.

The pattern detection is done in the last public function findPattern. This method encapsulates the whole routine as described previously, including feature detection, descriptors extraction, and matching with outlier filtration.

Let's conclude again with a brief list of the steps we performed: 1. Converted input image to grayscale.

Detected features on the query image using our feature-detection algorithm. Extracted descriptors from the input image for the detected feature points.

Matched descriptors against pattern descriptors. Used cross-checks or ratio tests to remove outliers. Found the homography transformation using inlier matches.

Refined the homography by warping the query image with homography from the previous step. Found the precise homography as a result of the multiplication of rough and refined homography.

Transformed the pattern corners to an image coordinate system to get pattern locations on the input image.

Pattern pose estimation The pose estimation is done in a similar manner to marker pose estimation from the previous chapter. As usual we need 2D-3D correspondences to estimate the camera-extrinsic parameters.

We assign four 3D points to coordinate with the corners of the unit rectangle that lies in the XY plane the Z axis is up , and 2D points correspond to the corners of the image bitmap.

This program will find the internal lens parameters such as focal length, principal point, and distortion coefficients using a series of pattern images.

You can use the exact file names, such as img1. The generated file imagelist. Also, the calibration tool can take images from a regular web camera.

We specify the dimensions of the calibration pattern and input and output files where the calibration data will be written.

With this data we can create an instance of the camera-calibration object using the following code for calibration: CameraCalibration calibration The estimated perspective transformation will differ from the transformation that the camera has.

This will cause the augmented objects to look like they are too close or too far. The following is an example screenshot where the camera calibration was changed intentionally: As you can see, the perspective look of the box differs from the overall scene.

You are probably familiar with this function because we used it in the marker-based AR too. We need the coordinates of the pattern corners on the current image, and its reference 3D coordinates we defined previously.

The cv::solvePnP function can work with more than four points. Also, it's a key function if you want to create an AR with complex shape patterns.

Of course, the homography estimation is not applicable here. We take the reference 3D points from the trained pattern object and their corresponding projections in 2D from the PatternTrackingInfo structure; the camera calibration is stored in a PatternDetector private field.

Now it's time to show how to put these algorithms into a real application. So our goal for this section is to show how to use OpenCV to capture a video from a web camera and create the visualization context for 3D rendering.

As our goal is to show how to use key features of marker-less AR, we will create a simple command-line application that will be capable of detecting arbitrary pattern images either in a video sequence or in still images.

To hold all image-processing logic and intermediate data, we introduce the ARPipeline class. It's a root object that holds all subcomponents necessary for augmented reality and performs all processing routines on the input frames.

The processFrame function implements pattern detection and the person's pose-estimation routine. The return value indicates the success of pattern detection.

You can get the calculated pattern pose by calling the getPatternLocation function. But unlike the iOS environment, where we had to follow the iOS application architecture requirements, we now have much more freedom.

On Windows and Mac you can choose from many 3D engines. In this chapter, we will learn how to create crossplatform 3D visualization using OpenCV.

This means you can now easily render any 3D content in OpenCV. As of the current version OpenCV 2. We cannot guarantee it, but OpenGL may be enabled by default in future releases.

If so, there will be no need to build OpenCV manually. You will need either command-line git tools or the GitHub Application installed on your computer to perform this step.

You will need a CMake application to complete this step. When this process is done, you can configure the sample project using the new OpenCV library you've just built.

We will use Markerless AR here. This call will create a window with the specified name. The first argument sets the window name, the second is a callback function, and the third optional argument will be passed to the callback function.

To capture video from either a webcam or a video file, we can use the cv::VideoCapture class, as shown in the Accessing the webcam section from Chapter 1, Cartoonifier and Skin Changer for Android.

The frame rendering starts by drawing a background with an orthography projection. Then we render a 3D model with the correct perspective projection and model transformation.

If so, we proceed to drawing a background, otherwise we create a new 2D texture by calling glGenTextures. To draw a background, we set up an orthographic projection and draw a solid rectangle that covers all the screen viewports.

This rectangle is bound with a texture unit. Its content is uploaded to the OpenGL memory beforehand. This function is identical to the function from the previous chapter, so we will omit its code here.

After drawing the picture from a camera, we switch to drawing an AR. It's necessary to set the correct perspective projection that matches our camera calibration.

To prove that our pose estimation works correctly, we draw a unit coordinate system in the pattern position. Almost all things are done.

We create a pattern-detection algorithm and then we estimate the pose of the found pattern in 3D space, a visualization system to render the AR.

We create two functions that help us with this. Both of them have a very common routine of image processing, pattern detection, scene rendering, and user interaction.

Returns true. Then we initialize ARDrawingContext using calibration again.

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