The computer vision course involves the development of algorithms and software that have the potential to mimic a biological organism's ability to see. Topics include the physics of vision and its computational modeling, mathematical techniques for representing and reasoning with curves, surfaces, and volumes, image formation and sensing, camera model, thin lens model, lighting and reflectance, image capture and processing including edge finding, corner detection, image segmentation and texture analysis, image reflectometry involving color, image irradiance, and reflectance map, image analysis techniques such as convolution, filtering, noise, derivatives, and smoothing, scale space and SIFT, motion estimation and optic flow, 3D vision including shape from shading and shape from texture and defocus, geometric camera calibration, homographies, structure from motion, epipolar geometry and estimating of fundamental matrix, and dense stereo correspondence.