This course is intended for advanced undergraduate students. We assume students have a rudimentary understanding of linear algebra, calculus, and are able to program in some type of structured language.

Computer Vision consists of inferring properties of the world based on one or more digital images. Provides background in image processing and image formation. Focus on algorithms for image and video analysis based on color, texture, shading, stereo, and motion.

Contents

Introduction to Computer Vision
Basic definitions. Low-level image analysis methods, including image formation, edge detection, feature detection, and image segmentation.
3D Vision and motion analysis
Methods for reconstructing three-dimensional scene information using techniques such as depth from stereo, structure from motion, and shape from shading. Motion and video analysis.
Object recognition
Recognition Processes. Direct Comparison. Alignment methods. Invariant properties methods. Parts decompositions method. Hough transform.
Image synthesis
Computer graphics topics involving computational photography and image-based rendering. Local rendering, Phong model. Advanced rendering techniques, topics include ray casting, ray tracing, and radiosity.