This chapter will discuss Unit 5 Computer Vision Artificial Intelligence Class 10. Here we go!
Topics Covered
Unit 5 Computer Vision
This unit is divided into two parts.
- Topics to be assessed in Theory
- Topics to be assessed in Practical
Unit 5 Computer Vision – Topics to be assessed in theory
The following topics are to be assessed in theory:
| S.No | Topic |
| 1 | Introduction to Computer Vision |
| 2 | Applications of Computer Vision |
| 3 | Getting Started with Computer Vision |
| 4 | Computer Vision Tasks — Classification — Classification + Localization — Object Detection — Instance Segmentation |
| 5 | Basics of Images |
| 6 | Basics of Pixels |
| 7 | Resolution |
| 8 | Pixel Value |
| 9 | Grayscale Images |
| 10 | RGB Images |
| 11 | Image Features |
Unit 5 Computer Vision – Topics to be assessed in practical
| S.No | Topic |
| 1 | Introduction to Open CV |
| 2 | Convolution |
| 3 | What is Kernel? |
| 4 | Convolution Neural Network (CNN) |
So let us begin Unit 5 Computer Vision with topics to be assessed in Theory. Here we go!
Watch this video for exaplation:
Introduction to Computer Vision
All human beings and animals can see things. After seeing something, anyone can create a picture of an imagination in his mind. This is called a vision. This is possible due to the eyes.
Computer Vision is one of the domains of Artificial Intelligence that enables machines to see images or visualize data, process and analyze them based on algorithms and methods to analyze actual phenomena with images.
To understand this domain you have played this game in class 9, click on below image to play once again and understand this domain functions:
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.
Answer these questions after playing this game:
- Did you manage to win?
- What strategy did you apply to win this game?
- Was the computer able to identify the items you brought in front of it?
- Did the lighting of the room affect on the items’ identification by machine?
Application of Computer Vision
The concept of Computer Vision was introduced in the 1970s. Today there are many applications around us which is based on Computer Vision. So let us take a quick walk-through about few of them:
Facial Recognition
It is a very important application in terms of security and human recognition such as quests or visitors, log maintenance, student attendance as well employee attendance, etc. Sometimes it can also be helpful to lock and unlock devices. It plays a vital role in smart homes and smart cities concept.
Face Filters
Image Souce: Filter Snapchat Face PNG File HD from FreePNGimg.com
I know that you are fond of taking snaps on Snapchat and posting stories on Instagram, Isn’t it? Yes, these apps use Computer Vision features to apply various features on a face. It takes face as input through a camera and is able to identify the dynamics of your face and applies a selected filter.
Google search by image
Day by day search engines become more and more advanced. Apart from text-based searches Google also provides information based on images. This uses Computer Vision and compares the different features of an input image to the database of images and provides the search result as well as analyze various features of the image.
Computer Vision in retail
To enhance customer experience more fruitful Computer Vision is a handy tool. It can monitor the customer’s movements through the store, analyze the navigations, and detect walking patterns. It can be also helpful for inventory management systems. It provides a very accurate estimate of the items available in the store. It also recommends the appropriate space for an item placement in the store.
Self-driving cars
Image Credit – Freepik
Almost every car manufacturer aims to develop self-driving cars. This involves computer vision to identify various objects, gain insights into navigation routes, and monitor the environment. Hence, computer vision is the fundamental technology behind this.
Medical Imaging
In medical imaging, Computer Vision is used to create and analyze images and assists doctors in interpretation and understanding. It is capable of reading and analyzing 2D images and converting them into 3D and helps medical professionals to determine the patient’s health conditions.
Google Translate App
Understanding the language that you don’t know, becomes easier now through the Google Translate app. Google Translate app allows one to read the language that comes in front of the camera and translate it according to the user’s need. It uses Optical Character Recognition to see the image and Augmented Reality to overlay accurate translation.
Hence, Computer Vision can be defined as the power of seeing things by machine. It is one of the domains of Artificial Intelligence. It handles images in the machine and processes them to build Artificial Intelligence models and applications.
Computer Vision Tasks
Certain tasks can be performed on images. The machine can extract some basic information from images which can be used to predict something or can be used as data input for various AI applications. The computer vision tasks are further divided into two categories:
Classification
It is an act of assigning an appropriate label for an image from a collection of pre-assigned labels. For example – If a computer has to identify an apple from mango, banana, and apple. By some calculations, the system can recognize and assign a label as it is an apple or it is a banana or it is a mango.
Classification + Localisation
This model identifies the object and its location in the object. This process is only for single projects.
Object Detection
It includes finding the instances of an object in the image. It can extract features from the image and recognize the cases in the image. It is used in image retrieval and automated vehicle parking systems.
Instance Segmentation
Instance segmentation is a computer vision technique that identifies and outlines individual objects in an image, providing a pixel-wise mask for each. This advanced method surpasses traditional object detection by distinguishing multiple objects of the same category, such as two cars parked side by side.
Basics of images
We come across many images every day, whether on our mobile devices or computer screens. Yet, do we take a moment to consider some basic questions about how we engage with them so frequently?
Not sure of the answer? There’s no need for concern; in this section, we will look into the fundamental aspects of an image.
Basics of Pixels
The term “pixel” refers to a picture element. Digital photographs are made up of pixels, the smallest units of information in an image. Typically round or square, they are arranged in a two-dimensional grid. The accompanying enlarged section illustrates its pixel composition, showing that more pixels yield a representation that closely resembles the original image.
Resolution
The number of pixels in an image, often referred to as resolution, can be expressed as width by height (e.g., 1280×1024), indicating 1280 pixels horizontally and 1024 vertically. Another way to denote pixel count is as a single number, such as a 5-megapixel camera (1 megapixel = 1 million pixels). For the 1280×1024 resolution, this can also be calculated as 1280 x 1024 = 1,310,720 or 1.31 megapixels.
Pixel value
Each pixel in a computer-stored image has a value indicating its brightness and/or color. The most common format is the byte image, where this value is stored as an 8-bit integer ranging from 0 to 255. Here, zero represents no color (black) and 255 represents full color (white). The value of 255 arises because computer data uses the binary system, where each bit can be either a zero or a one. Each pixel uses 1 byte (8 bits), which allows for 256 possible values, from 0 to 255.
Try to understand this using the tables given.
Pattern for 1-bit
| A | B |
| 0 | 1 |
| 1 | 0 |
No. of Patterns: 2 ^ 1 = 2
Pattern for 2-bits
| A | B |
| 0 | 0 |
| 0 | 1 |
| 1 | 0 |
| 1 | 1 |
No. of patterns: 2 ^ 2 = 4
Pattern for 3-Bits
| A | B | C |
| 0 | 0 | 0 |
| 0 | 0 | 1 |
| 0 | 1 | 0 |
| 0 | 1 | 1 |
| 1 | 0 | 0 |
| 1 | 0 | 1 |
| 1 | 1 | 0 |
| 1 | 1 | 1 |
No. patterns : 2 ^ 3 = 8
Similarly for 4 bits: 2 ^ 4 =16, 2 ^ 5= 32, 2 ^ 6 = 64, 2 ^ 7 = 128 and 2 ^ 8 = 256.
Grayscale Images
Grayscale images consist of various shades of gray, devoid of any discernible color. The darkest shade is black, representing a complete absence of color or a pixel value of zero.
Conversely, the lightest shade is white, indicating the full presence of color or a pixel value of 255. Intermediate gray shades are depicted by equal brightness levels across the three primary colors.
In a grayscale image, each pixel occupies 1 byte and is organized in a two-dimensional array. The dimensions of a grayscale image are determined by its height and width.
A grayscale image consists of pixels, each occupying 1 byte, organized in a two-dimensional array with a single channel. The dimensions of a grayscale image are determined by its Height multiplied by its Width.
RGB Images
All the colored images available around us in the machines are made from RGB patterns. RGB is a set of primary colors Red, Green, and Blue. All colors which are available in the image are made with a combination of Red, Green, and Blue color patterns.
After observing this, try to answer these questions:
- If R=G=B=255, then which color is made?
- If R=G=B=0, then which color is made?
- If R=255, G=0, B=0, then which color is made?
- If R=0, G=255, B=0, then which color is made?
- If R=0, G=0, B=255, then which color is made?
- If R=255, G=255, B=0, then which color is made?
- If R=0, G=255, B=255, then which color is made?
- If R=255, G=0, B=255, then which color is made?
- What is the combination of your favorite color?
- Write the combination to form an orange color.
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.
Follow this link to get important MCQ questions.