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CBSE Class 10 · Artificial Intelligence · board exam 2027

Chapter 5: Computer Vision — notes

The chapter’s 7 parts in the book’s order — the first idea of each and the points Medha Ma’am writes on the board — and the summary at the end. The full lesson and a check question for each part are in the app.

This chapter’s important questions →

  1. 1. What is Computer Vision?

    Okay, so you already know AI lets computers mimic human intelligence.

    • • Computer Vision: branch of AI that lets machines see
    • • Human Vision: Eye (sensing) + Brain (interpreting)
    • • Computer Vision Vs Image Processing
    • • CV: object detection, handwriting recognition
    • • Image Processing: rescaling, brightness, tones
    • • CV is superset, Image Processing is subset

  2. 2. Applications of Computer Vision

    Okay, so — Computer Vision started way back in the 1970s.

    • • CV applications: Facial Recognition, Face Filters
    • • Google Search by Image, Retail (tracking, inventory)
    • • Self-Driving Cars: object ID + navigation + monitoring
    • • Medical Imaging: 2D scans to interactive 3D models
    • • Google Translate App: OCR + augmented reality

  3. 3. Computer Vision Tasks

    Okay, so — behind every Computer Vision application, there are certain tasks that pull information out of an image.

    • • Classification: one label from fixed categories
    • • Classification + Localisation: label + location (single object)
    • • Object Detection: finds instances of real objects
    • • Instance Segmentation: category + pixel-level label

  4. 4. Pixels, Resolution and Pixel Value

    Every digital photograph is made up of pixels, short for picture elements, the smallest unit of information that makes up a picture, usually arranged in a 2-dimensional grid.

    • • Pixel = picture element, smallest unit of a picture
    • • Resolution: width x height, e.g. 1280x1024
    • • Megapixel = a million pixels
    • • 1280 x 1024 = 1,310,720 = 1.31 megapixels
    • • Pixel value: 8-bit integer, range 0 to 255
    • • 0 = black (no colour), 255 = white (full colour)

  5. 5. Grayscale and RGB Images

    Okay, so let's talk about grayscale and RGB images.

    • • Grayscale: shades of gray, no colour
    • • 0 = black (no colour), 255 = white (full colour)
    • • Grayscale size = Height x Width, 1 byte per pixel
    • • RGB image = Red + Green + Blue channels
    • • Each channel: pixel values 0 to 255
    • • Each RGB pixel has 3 values together

  6. 6. Image Features and Convolution

    Okay, so — let's talk about features in an image.

    • • Feature: point, edge, or object info in an image
    • • Flat patch: hard to find (looks same everywhere)
    • • Edge patch: same along the edge, still hard
    • • Corner patch: unique, easiest to find
    • • Convolution: I * K (image array x kernel, summed)
    • • Kernel: matrix slid across image

  7. 7. Convolutional Neural Network (CNN)

    Okay, so let's talk about the Convolutional Neural Network, or CNN.

    • • CNN: Deep Learning algorithm, assigns weights to image parts
    • • Layers: Convolution, ReLU, Pooling, Fully Connected
    • • Convolution Layer: kernels produce feature map
    • • ReLU: removes negative numbers, keeps positive
    • • Pooling: Max or Average, shrinks image, keeps key features
    • • Fully Connected: flattens, gives label probability

Summary

Okay, so let's go back over everything we covered in this chapter. We started with Computer Vision itself — it's the branch of Artificial Intelligence that lets machines see, process and understand images and videos. And we also saw how it's different from Image Processing. Then we looked at real applications — Facial Recognition, Face Filters, Google Search by Image, Retail, Self-Driving Cars, Medical Imaging, and Google Translate. Remember those? After that, we got into the main Computer Vision tasks — Classification, Classification plus Localisation, Object Detection, and Instance Segmentation. You also learned how an image is really built from pixels, how resolution works, and how pixel values run from 0 to 255. We talked about Grayscale images and RGB images too, and how they're different. Then came a nice discovery — corners make the best image features, and edges come right after. And we saw how Convolution uses a kernel to change an image. Finally, we put it all together with the Convolutional Neural Network — its Convolution, ReLU, Pooling, and Fully Connected layers, all working together to classify an image. That's a lot we've covered, isn't it? Which of these topics did you find the most interesting?

Ask Medha · Medha Ma’am — CBSE Class 10 Artificial Intelligence, Chapter 5. Written from the textbook, checked twice.

Questions parents and students ask

How do I get the "Computer Vision" notes as a PDF?
Press "Save as PDF" — the phone’s print sheet opens; choose "Save as PDF" there. The PDF holds the notes only.
Where are these notes written from?
From the chapter’s own textbook text, part by part, in the book’s own terms; a second, independent model then checked every part against the book. The full lesson, a check question for each part and the practice are in the Ask Medha app.

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