Image Processing and Feature Extraction
This module covers the basic image processing techniques such as filtering, thresholding, and segmentation. Students will learn how to extract features from images using traditional feature descriptors, such as SIFT and HOG.
Convolutional Neural Networks (CNNs)
This module covers the fundamental concepts of CNNs, including convolutional layers, pooling layers, and fully connected layers. Students will learn how to design and train CNNs for various computer vision tasks, such as image classification and object detection.
Object Detection and Recognition
This module covers the state-of-the-art techniques in object detection and recognition, such as YOLO and Faster R-CNN. Students will learn how to detect and recognize objects in images and videos using these techniques.
Applications of Computer Vision
This module covers real-world applications of computer vision, such as facial recognition, autonomous driving, and augmented reality. Students will learn how to apply computer vision techniques to solve practical problems in different domains.