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“Computer vision and machine learning have really started to take off, but for most people, the whole idea of what is a computer seeing when it's looking at an image is relatively obscure.”

-Mike Krieger

GRADE - 10

ARTIFICIAL INTELLIGENCE - UI 6 COMPUTER VISION

Name : ____________________________________________

Grade : ____________________ Section _______________

School : ____________________________________________

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NAME OF THE CONTRIBUTORS

1. Mr. Gaurav Kumar Jain (DPSG INT) PLC & GSG – AI IX - X 2. MS. Urvashi Gupta (DPSG SLO)

3 Ms. Seema Pathak (DPSG MRD)

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Introduction to the Unit of Inquiry ... 1

Historical Context/ Anecdotal – how does it change the world, some interesting facts ... 2

Setting the Scope of Inquiry - Why and how much do we need to know ... 4

The UI Design & Journey ... 4

The Structure of UI ... 5

Session Flow - Key Milestones ... 6

Session1: Introduction ... 6

Session 2: Applications of Computer Vision... 7

Session 3: Computer Vision Tasks ... 7

Session 4: Images ... 8

Session 5: Image Feature ... 10

Session 6: Introduction to OpenCV ... 11

Session 7: Convolution ... 11

Session 8: Convolution Explained in Detail ... 13

Session 9: Assessment ... 15

Unit Time Plan 2021-22 ... 16

Resources – Links, Names of Reading Resources ... 17

Independent Study - Assignments ... 17

Glossary ... 18

Explore More… ... 18

References ... 18

Student’s Space ... 19

Parent’s Space ... 19

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DPSGS | 1

Hook & Context Setting

Artificial Intelligence is a technology which completely depends on data. It is the data which is fed into the machine which makes it intelligent. And depending upon the type of data we have; AI can be classified into three broad domains:

Introduction to the Unit of Inquiry

As we all know, artificial intelligence is a technique that enables computers to mimic human intelligence.

As humans we can see things, analyse it and then do the required action on the basis of what we see.

But can machines do the same? Can machines have the eyes that humans have? If you answered Yes, then you are right. The Computer Vision domain of Artificial Intelligence, enables machines to see through images or visual data, process and analyse them based on algorithms and methods in order to analyse actual phenomena with images

Comic Strip/ Video

https://qrgo.page.link/EPdNW

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Historical Context/ Anecdotal – how does it change the world, some interesting facts

More than 50 years ago Marvin Minsky made the first attempt to mimic the human brain, triggering further research into computers’ ability to process information to make intelligent decisions. Over the years, the process of automating image analysis led to the programming of algorithms. However, it was only from 2010 onward, when there was acceleration in deep learning techniques. In 2012 Google Brain built a neural network of 16,000 computer processors which could recognise pictures of cats using a deep learning algorithm

Listed below are the major milestones in the computer vision theme, as identified by Global Data 1959 – The first digital image scanner was invented by transforming images into grids of numbers.

1963 – Larry Roberts, the father of CV, described the process of deriving 3D info about solid objects from 2D photographs.

1966 – Marvin Minksy instructed a graduate student to connect a camera to a computer and have it described what it sees.

1980 – Kunihiko Fukushima built the ‘neocognitron’, the precursor of modern Convolutional Neural Networks.

1991-93 – Multiplex recording devices were introduced, together with cover video surveillance for ATM machines.

2001 – Two researchers at MIT introduced the first face detection framework (Viola-Jones) that works in real-time.

2009 – Google started testing robot cars on roads.

2010 – Google released Goggles, an image recognition app for searches based on pictures taken by mobile devices.

2010 – To help tag photos, Facebook began using facial recognition.

2011 – Facial recognition was used to help confirm the identity of Osama bin Laden after he is killed in a US raid.

2012 – Google Brain’s neural network recognized pictures of cats using a deep learning algorithm.

2015 – Google launched open-source Machine learning-system TensorFlow.

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DPSGS | 3 2016 – Google DeepMind’s AlphaGo algorithm beat the world Go champion.

2017 – Waymo sued Uber for allegedly stealing trade secrets.

2017 – Apple released the iPhone X in 2017, advertising face recognition as one of its primary new features.

2018 – Alibaba’s AI model scored better than humans in a Stanford University reading and comprehension test.

2018 – Amazon sold its real time face recognition system Recognition to police departments.

2019 – The Indian government announced a facial recognition plan allowing police officers to search images through mobile app.

2019 – The US added four of China’s leading AI start-ups to a trade blacklist.

2019 – The UK High Court ruled that the use of automatic facial recognition technology to search for people in crowds is lawful.

2020 – Intel will launch the Intel Xe graphics card pushing into the GPU market.

2025 – By this time, regulation in FR will significantly diverge between China and US/Europe.

2030 – At least 60% of countries globally will be using AI surveillance technology (it is currently 43%

according to CEIP).

Essential Questions

• What all Computer vison-based technologies have humans developed till date?

• What is Goggles?

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Setting the Scope of Inquiry - Why and how much do we need to know Why?

With petabytes of visual data generated every day, computer vision is the field of Artificial Intelligence with the greatest potential for growth in the short, medium and long term, since it is impossible to process such a volume of information without the support of neural networks, as well as the developments to come.

How?

• How to get started with Computer Vision?

Techniques used in Computer Vision

Things to explore before the unit of inquiry. How will this impact your future course of learning – Previous topic and next topic

Previous Topic:

• Data Science

• Applications of Data Science

• Understand Data Collection

• Python Statistics

Next Topic:

• To get started with Computer Vision

• Applications of Computer Vision

• OpenCV for Computer Vision

• Understanding Convolution technique

The UI Design & Journey

1. Introduction to Computer Vision.

2. Applications of Computer Vision 3. Computer Vision Tasks

4. What are Images?

5. Introduction to OpenCV 6. Convolution

7. UI Assessment

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DPSGS | 5

The Structure of UI

Introduction

Applications of Computer Vision

Computer Vision Tasks

Images

Image Features

Introduction To OpenCV

Convolution

Convolution explained in detail

UI Assessment

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Session Flow - Key Milestones Session1: Introduction

The Computer Vision domain of Artificial Intelligence enables machines to see through images or visual data, process and analyse them based on algorithms and methods in order to analyse actual phenomena with images.

Video Link-

https://qrgo.page.link/Wnmz3 Activity1- Emoji Scavenger Hunt

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DPSGS | 7 Visit the link: https://emojiscavengerhunt.withgoogle.com/ Go to the link and try to play the game of Emoji Scavenger Hunt. The challenge here is to find 8 items within the time limit to pass.

Session 2: Applications of Computer Vision

https://qrgo.page.link/HM5r2

Session 3: Computer Vision Tasks

The various applications of Computer Vision are based on a certain number of tasks which are performed to get certain information from the input image which can be directly used for prediction or forms the base for further analysis. The tasks used in a computer vision application are

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Classification. Image Classification basically means identifying what class the object belongs to. For example, in the image shown below, there are objects present belonging to various classes such as trees, huts, giraffe, etc. The machine or deep learning model would determine that the animal detected in the image belongs to class giraffe with the highest probability.

Classification + Localisation:This is the task which involves both processes of identifying what object is present in the image and at the same time identifying at what location that object is present in that image.

It is used only for single objects.

Object Detection: is the ability to detect or identify objects in any given image correctly along with their spatial position in the given image, in the form of rectangular boxes (known as Bounding Boxes) which bound the object within it. An example is shown below which detects objects such as laptop, glasses, notebook, coffee and iphone in their Bounding Boxes.

Instance Segmentation: It basically identify different instances given in the image with their boundaries intact. For example, the image below showcases that the model has tried to identify each object instance in the image and recognized their boundary at the deep pixel level.

https://qrgo.page.link/PV1cf

Session 4: Images

What is Pixel?

A pixel is the smallest unit of a digital image or graphic that can be displayed and represented on a digital display device. A pixel is the basic logical unit in digital graphics. Pixels are combined to form a complete image, video, text, or any visible thing on a computer display.

A pixel is also known as a picture element (pix = picture, el = element).

What is Resolution?

Resolution is a measure used to describe the sharpness and clarity of an image or picture. It is often used as a metric for judging the quality of monitors, printers, digital images and various other hardware and software technologies.

What is Pixel Value?

Each of the pixels that represents an image stored inside a computer has a pixel value which describes how bright that pixel is, and/or what colour it should be. The most common pixel format is the byte image, where

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DPSGS | 9 this number is stored as an 8-bit integer giving a range of possible values from 0 to 255. Typically, zero is to be taken as no colour or black and 255 is taken to be full colour or white.

Why do we have a value of 255 ? In the computer systems, computer data is in the form of ones and zeros, which we call the binary system. Each bit in a computer system can have either a zero or a one.

Since each pixel uses 1 byte of an image, which is equivalent to 8 bits of data. Since each bit can have two possible values which tells us that the 8 bit can have 255 possibilities of values which starts from 0 and ends at 255.

What are Grayscale Images?

A grayscale (or graylevel) image is simply one in which the only colors are shades of gray. The reason for differentiating such images from any other sort of color image is that less information needs to be provided for each pixel. In fact a `gray' color is one in which the red, green and blue components all have equal intensity in RGB space, and so it is only necessary to specify a single intensity value for each pixel, as opposed to the three intensities needed to specify each pixel in a full color image.

Often, the grayscale intensity is stored as an 8-bit integer giving 256 possible different shades of gray from black to white. If the levels are evenly spaced then the difference between successive gray levels is significantly better than the gray level resolving power of the human eye.

What are RGB Images?

An RGB image, sometimes referred to as a truecolor image, is stored in MATLAB as an m-by-n-by-3 data array that defines red, green, and blue color components for each individual pixel. RGB images do not use a palette. The color of each pixel is determined by the combination of the red, green, and blue intensities stored in each color plane at the pixel's location. Graphics file formats store RGB images as 24-bit images, where the red, green, and blue components are 8 bits each. This yields a potential of 16 million colors. The precision with which a real-life image can be replicated has led to the commonly used term truecolor image.

An RGB MATLAB array can be of class double, uint8, or uint16. In an RGB array of class double, each color component is a value between 0 and 1. A pixel whose color components are (0,0,0) displays as black, and a pixel whose color components are (1,1,1) displays as white. The three color components for each pixel are stored along the third dimension of the data array. For example, the red, green, and blue color components of the pixel (10,5) are stored in RGB(10,5,1), RGB(10,5,2), and RGB(10,5,3), respectively.

Time for Assessment [Assessment- 2 marks]

Go to this online link https://www.w3schools.com/colors/colors_rgb.asp. Based on this online tool, try and answer all the below mentioned questions

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1) What is the output colour when you put R=G=B=255?

2) What is the output colour when you put R=G=B=0?

Time for Assessment [Assessment- 2 marks]

Go to the following link www.piskelapp.com and create your own pixel art. Try and make a GIF using the online app for your own pixel art.

Session 5: Image Feature

In computer vision and image processing, a feature is a piece of information which is relevant for solving the computational task related to a certain application. Features may be specific structures in the image such as points, edges or objects.

For example:

Imagine that your security camera is capturing an image. At the top of the image we are given six small patches of images. Our task is to find the exact location of those image patches in the image.

Take a pencil and mark the exact location of those patches in the image.

Time for Assessment [Assessment- 2 marks]

• Were you able to find the exact location of all the patches?

• Which one was the most difficult to find?

Let’s Reflect:

Let us take individual patches into account at once and then check the exact location of those patches.

For Patch A and B: The patch A and B are flat surfaces in the image and are spread over a lot of area.

They can be present at any location in a given area in the image.

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DPSGS | 11 For Patch C and D: The patches C and D are simpler as compared to A and B. They are edges of a building and we can find an approximate location of these patches but finding the exact location is still difficult.

This is because the pattern is the same everywhere along the edge.

For Patch E and F: The patches E and F are the easiest to find in the image. The reason being that E and F are some corners of the building. This is because at the corners, wherever we move this patch it will look different.

https://qrgo.page.link/CT6c6

Session 6: Introduction to OpenCV

OpenCV is a huge open-source library for computer vision, machine learning, and image processing.

OpenCV supports a wide variety of programming languages like Python, C++, Java, etc. It can process images and videos to identify objects, faces, or even the handwriting of a human. When it is integrated with various libraries, such as Numpy which is a highly optimized library for numerical operations, then the number of weapons increases in your Arsenal i.e whatever operations one can do in Numpy can be combined with OpenCV.

This OpenCV tutorial will help you learn the Image-processing from Basics to Advance, like operations on Images, Videos using a huge set of Opencv-programs and projects.

To install OpenCV library, open anaconda prompt and then write the following command:

pip install opencv-python

https://qrgo.page.link/D1hgZ

Session 7: Convolution

Convolution is a general-purpose filter effect for images. It is a matrix applied to an image and a mathematical operation comprised of integers. It works by determining the value of a central pixel by adding the weighted values of all its neighbors together. The output is a new modified filtered image.

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Time for Assessment [Assessment- 2 marks]

Go to the link http://matlabtricks.com/post-5/3x3-convolution-kernels-with-online-demo and at the bottom of the page click on load “Click to Load Application”

Once the application is loaded try different filters and apply it on the image. Observe how the value of the kernel is changing for different filters. Try these steps

1) Change all to positive values 2) Change all to negative values

3) Have a mixture of negative and positive values

Let us follow the following steps to understand how a convolution operator works. The steps to be followed are:

Try experimenting with the following values to come up with a theory:

1) Make 4 numbers negative. Keep the rest as 0.

2) Now make one of them as positive.

3) Observe what happens.

4) Now make the second positive.

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DPSGS | 13

Session 8: Convolution Explained in Detail

Convolution is a simple Mathematical operation which is fundamental to many common image processing operators. Convolution provides a way of `multiplying together' two arrays of numbers, generally of different sizes, but of the same dimensionality, to produce a third array of numbers of the same dimensionality.

An (image) convolution is simply an element-wise multiplication of image arrays and another array called the kernel followed by sum.

As you can see here, I = Image Array K = Kernel Array

I * K = Resulting array after performing the convolution operator

Note: The Kernel is passed over the whole image to get the resulting array after convolution What is a Kernel?

A Kernel is a matrix, which is slid across the image and multiplied with the input such that the output is enhanced in a certain desirable manner. Each kernel has a different value for different kind of effects that we want to apply to an image.

In Image processing, we use the convolution operation to extract the features from the images which can le later used for further processing especially in Convolution Neural Network (CNN), about which we will study later in the chapter.

In this process, we overlap the centre of the image with the centre of the kernel to obtain the convolution output. In the process of doing it, the output image becomes smaller as the overlapping is done at the edge row and column of the image. What if we want the output image to be of exact size of the input image, how can we achieve this?

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To achieve this, we need to extend the edge values out by one in the original image while overlapping the centres and performing the convolution. This will help us keep the input and output image of the same size.

While extending the edges, the pixel values are considered as zero.

https://qrgo.page.link/gpDDW

Time for Assessment [Assessment- 2 marks]

In this section we will try performing the convolution operator on paper to understand how it works. Fill the blank places of the output images by performing the convolution operation

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DPSGS | 15

Session 9: Assessment

• Assessment through MS Form

• The assessment will be assessed based on following Assessment Criteria

:

Topic Marking

Scheme (20)

Testing Knowledge

Images 2 Identification,

Application

Images 2 Identification,

Application

Image Features 2 Identification

Convolution 2 Memory, Application

Convolution in detail 2 Memory, Application

Quiz 10 Memory, Application

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Unit Time Plan 2021-22

Class X

SUBJECT: ARTIFICIAL INTELLIGENCE COMPUTER VISION

[9 Sessions/Periods]

Content to be covered Instructional Strategy

[Mention clearly as to which methodology would be used for the particular session]

Lessons/

Sessions

• Introduction to Computer vision

Video & Activities followed with discussion 1

• Applications of Computer Vision Video followed with discussion 1

• Computer Vision tasks Video, brainstorming to find out the required

information 1

• Images General Discussion to develop an

Understanding followed by two Assessment

1

• Image Feature Videos, Examples + Assessment 1

• Introduction to OpenCV Video, brainstorming to find out the required

information 1

• Convolution Videos, Examples + Assessment 1

• Convolution Explained in Detail Videos, Examples + Assessment 1

• Individual Assessment Activity Quiz 1

TOTAL SESSION 9

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DPSGS | 17

Resources – Links, Names of Reading Resources

• https://machinelearningmastery.com/what-is-computer-vision/

• https://xd.adobe.com/ideas/principles/emerging-technology/what-is-computer-vision-how-does-it- work/

• https://www.udacity.com/course/introduction-to-computer-vision--ud810

Independent Study - Assignments

Quiz:

https://www.analyticsvidhya.com/computer-vision-assessment-test/

The Assessment Plan:

How will the learning be assessed – The Assessment Plan, ongoing, end of session and end of UOI, Assessment Criteria and Rubrics

Worksheet:

https://letsfindcourse.com/computer-graphics-mcq/computer-vision-mcq-questions-and- answers

Things To Do/ Explore More…

Any tasks that encourage application, exploration and experimentation

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Glossary

Terms Definition

Pixel A pixel is the smallest unit of a digital image or graphic that can be displayed and represented on a digital display device

Resolution A measure used to describe the sharpness and clarity of an image or picture

RGB Image A TrueColor image, is stored in MATLAB as an m-by-n-by-3 data array that defines red, green, and blue color components for each individual pixel OpenCV It is a huge open-source library for computer vision, machine learning, and

image processing.

Convolution It is a general-purpose filter effect for images

Kernel It is a matrix, which is slid across the image and multiplied with the input such that the output is enhanced in a certain desirable manner

Explore More…

• 10 Popular Computer Vision Courses

https://www.coursera.org/courses?query=computer%20vision

• Complete Data Science Tutorial Playlist

https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PLf7L7Kg8_FNxHATtLwDceyh72QQ L9pvpQ

References

• Verdict. (n.d.). Computer Vision Timeline. Retrieved from Verdict:

https://www.verdict.co.uk/computer-vision-timeline/

• Banerjee, A. (n.d.). https://ananya-banerjee.medium.com/different-computer-vision-tasks- b3b49bbae891. Retrieved from Computer Vision Tasks: https://ananya-

banerjee.medium.com/different-computer-vision-tasks-b3b49bbae891

• Techopedia. (n.d.). Techopedia. Retrieved from https://www.techopedia.com/definition/24012/pixel:

https://www.techopedia.com/definition/24012/pixel

• www.cbseacademic.nic.in

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DPSGS | 19

Student’s Space

My name is _______________________ from Grade ____________________

After reading my UI Plan Booklet

• I will prefer few things to be done differently such as

_______________________________________________________________________________

_______________________________________________________________________________

_______________________________________________________________________________

_______________________________________________________________________________

_______________________________________________________________________________

_______________________________________________________________________________

_______________________________________________________________________________

Parent’s Space

I am __________________________ Parent I have gone through the UI Plan Booklet

• I would like to suggest few changes

_______________________________________________________________________________

_______________________________________________________________________________

_______________________________________________________________________________

• I have a few questions/ clarifications

_______________________________________________________________________________

_______________________________________________________________________________

_______________________________________________________________________________

Parent Signature

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DPSGS | 21 My Notes:

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DPSGS | 23 My Notes:

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24 | DPSGS

DELHI PUBLIC SCHOOL GHAZIABAD SOCIETY

A-Block, 3rd Floor, MGF Metropolitan Mall, District Centre Saket, Saket, New Delhi – 110017

Office Phone : (+91)-11-473 42 000

Website : www.dpsgs.org

References

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