AI & Computer Vision :: Guide to Artificial Intelligence and Computer Vision for CBSE Class 12

AI & Computer Vision :: Guide to Artificial Intelligence and Computer Vision for CBSE Class 12

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  CBSE CLASS 12 ImportantConcept of Artificial Intelligence  Artificial Intelligence – Basics  Computer Vision – Basics & Applications  Digital Images, Pixels & Channels  Image Processing & Pre-processing  Edge Detection – Canny  Image Classification  Object Detection  Image Segmentation  Bounding Boxes  Image Features & Feature Extraction  Contextual Information  OpenCV & Important Functions  Matplotlib  CNN  YOLO & Modern Object Detection  Adversarial AI  Data Science Basics  Machine Learning & Model Evaluation  AI Ethics  AI Domains  Communication Skills Topic-wise Short Explanation 1. Artificial Intelligence Artificial Intelligence (AI) is the ability of computers or machines to perform tasks that normally require human intelligence, such as learning, reasoning, decision-making and problem-solving. 2. Computer Vision Computer Vision is a branch of AI that enables computers to understand and analyse images and videos. Examples: Face recognition, traffic sign detection, medical imaging. 3. Digital Image A digital image is made up of many tiny squares called pixels. Each pixel contains information about the image. 4. Pixel A pixel is the smallest unit of a digital image. Its position can be represented using row and column coordinates. Example: img[100, 200] → pixel at row 100 and column 200. 5. Image Channels An image channel contains information about one component of an image.  Colour image → usually 3 channels  RGB → Red, Green, Blue  OpenCV uses → BGR
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  6. Image Processing Imageprocessing means modifying or improving an image to make it more useful for analysis. Examples:  Resizing  Cropping  Grayscale conversion  Noise removal  Edge detection 7. Image Pre-processing Pre-processing prepares an image before it is given to a Computer Vision system. It can:  Remove noise  Improve quality  Resize the image  Enhance useful features 8. Noise Removal Noise means unwanted random variations or pixels in an image. Noise removal makes an image cleaner and easier to analyse. 9. Edge Detection Edge detection identifies boundaries or sharp changes in an image. Canny is a popular technique for detecting edges. Canny → Edge Detec on 10. Image Classification Image classification identifies what an image contains and assigns it to a class. Example: An image → Cat 11. Object Detection Object detection identifies what objects are present and where they are located in an image. It commonly uses bounding boxes. 12. Image Segmentation Image segmentation divides an image into meaningful regions or identifies the exact pixels belonging to different objects. 13. Bounding Box A bounding box is a rectangular box drawn around a detected object. It helps identify the object's location and size. 14. Feature Extraction Features are useful characteristics that help a Computer Vision system understand an image. Common features:  Colour  Shape  Texture  Edges 15. Contextual Information Context means information about the surroundings of an object or situation. It can help a Computer Vision system interpret an object more accurately. 16. OpenCV
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  OpenCV is aPython library widely used for Computer Vision and image processing. It can read, display, modify and analyse images and videos. Important functions Function Purpose cv2.imread() Reads an image cv2.imshow() Displays an image cv2.waitKey(0) Waits for a key press cv2.destroyAllWindows() Closes OpenCV windows 17. Matplotlib Matplotlib is a Python library used mainly for visualization, such as displaying images and creating graphs. 18. CNN CNN (Convolutional Neural Network) is a type of neural network designed especially for image-related tasks. It learns features progressively: Early layers → edges Middle layers → shapes/textures Deeper layers → complex objects 19. YOLO YOLO (You Only Look Once) is a modern object-detection method known for its fast detection, making it useful for real-time applications. 20. Adversarial AI An adversarial example is an input that has been slightly changed in a way that can cause an AI model to make an incorrect prediction. 21. Data Science Data Science involves collecting, cleaning, analysing and using data to find useful information and make predictions or decisions. 22. Data Cleaning Data cleaning means finding and correcting errors, missing values and inconsistencies in data. 23. Training and Testing Data  Training data → used to teach the model.  Testing data → used to check how well the trained model works on new data. 24. Overfitting Overfitting occurs when a model performs very well on training data but performs poorly on new or unseen data. 25. AI Ethics AI Ethics deals with developing and using AI in a fair, safe, transparent and responsible manner. Important concerns:  Bias  Privacy  Transparency  Fairness 26. AI Ethicist An AI Ethicist helps ensure that AI systems are developed and used responsibly and considers issues such as bias, fairness and privacy.
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  Communication Skills Good communicationinvolves listening carefully, understanding others, expressing ideas clearly and responding respectfully. Active Listening Active listening means paying attention to the speaker, understanding the message and responding appropriately. Respectful Communication It means expressing your ideas politely while respecting other people's opinions.