This course introduces computer vision, including fundamentals of image formation, camera imaging geometry, feature detection and matching, stereo, motion estimation, and tracking, image classification, and scene understanding. The topics covered include: introduction to computer vision using Python and MATLAB, image formation: cameras, image formation: Light, shade, and colour, convolution, filtering, and edge detection, segmentation and grouping, Features: corner detection, fitting: RANSAC, Hough transform, visual geometry: single view and epipolar geometry, machine learning with neural networks for object detection and Recognition, stereo vision: binocular and multi-view stereo, stereo vision; 3-D modeling; and statistical recognition, face detection, and recognition, visual tracking and optical flow.