Following courses are being offered by the CVAI lab this term
Course SyllabusThis course will present a broad, introductory survey intended to develop familiarity with the approaches to modeling and solving problems in computer vision. Mathematical modeling and algorithmic solutions for vision tasks will be emphasised. Image formation: camera geometry, radiometry, colour; Image features : points, lines, edges, contours, texture; Shape : object geometry, stereo, shape from cues; Motion : calibration, registration, Multiview geometry, optical Flow; approaches to grouping and segmentation; representation and methods for object recognition; applications;
Selected Topics in Image Processing
Course SyllabusImage Processing fundamentals, Image Segmentation and clustering: Mean-shift, Graph cut, Image Pyramids and texture analysis, Scale Space theory: SIFT, Visual Tracking: Mean-shift, Particle Filters, feature-based, Background Modeling, Surveillance and Monitoring: Event detection/recognition, Face Detection and Recognition, Motion analysis and segmentation, Basics of 3-D vision.
Linear and Non-Linear Optimization
Course SyllabusNecessary and sufficient conditions for optima; convex analysis; unconstrained optimization; descent methods; steepest descent, Newton’s method, quasi Newton methods, conjugate direction methods; constrained optimization; Kuhn-Tucker conditions, quadratic programming problems; algorithms for constrained optimization; gradient projection method, penalty and barrier function methods, linear programming, simplex methods; duality in optimization, duals of linear and quadratic programming problems