Computer Vision

Image Processing Fundamentals: Review of image processing techniques, Classical filtering operations, Thresholding Techniques, Edge detection techniques, Corner and interest point detection, Mathematical Morphology, Texture.

Shapes and Regions: Binary shape analysis, connectedness, object labeling and counting, size filtering, distance functions, Skeletons and thinning, deformable shape analysis, Boundary tracking procedure, active contours, Shape model and shape recognition, centroidal profiles, handling occlusion, boundary length measure, boundary descriptors, chain codes, Fourier descriptors, region descriptors.

Hough Transform: Line detection, Hough Transform (HT) for line detection, foot of normal method, line localization, line fitting, RANSAC for straight line detection, HT based circular object detection, accurate center location, speed problem, ellipse detection, Case Studies: Human Iris location, hole detection, generalized Hough Transformation (GHT), Spatial matched filtering, GHT for ellipse detection, object location, GHT for feature collation.

Methods for 3D vision, projection schemes, shape from shading, Photometric stereo, shape from texture, shape from focus, active range finding, surface representation, point-based representation, Volumetric representation, 3D object recognition, 3D reconstruction. Introduction to motion, triangulation, bundle adjustment, translational alignment, parametric motion, Spline-based motion, optional flow, layered motion.

Photo Album: Face detection, face recognition, Eigen faces, Active appearance and 3D shape models for face applications. Surveillance: Foreground and background separation, Particle filters, Chamfer matching, tacking and occlusion, combining view from multiple cameras, Human gait analysis. In vehicle vision system: locating roadways, road marking, identifying road signs, locating pedestrians.