The course is an introduction to 2D and 3D computer vision. Topics include: cameras models, the geometry of multiple views; shape reconstruction methods from visual cues: stereo, shading, shadows, contours; low-level image processing methodologies such as edge detection, feature detection; mid-level vision techniques (segmentation and clustering); Basic high-level vision problems: face detection, object and scene recognition, object categorization, and human tracking.
1) Szeliski, Richard. Computer Vision: Algorithms and Applications. London: Springer, 2011.
2) Forsyth, David, and Jean Ponce. Computer Vision: a Modern Approach. Upper Saddle River, NJ: Prentice Hall, 2003.
3) Hartley, Richard, and Andrew Zisserman. Multiple View Geometry in Computer Vision. Cambridge, UK: Cambridge UP, 2002.