Computer Science 280

Title Computer Vision
Units 3
Prerequisites Knowledge of linear algebra and calculus. Mathematics 1A-1B, 53, 54 or equivalent.
Description Paradigms for computational vision. Relation to human visual perception. Mathematical techniques for representing and reasoning, with curves, surfaces and volumes. Illumination and reflectance models. Color perception. Image segmentation and aggregation. Methods for bottom-up three dimensional shape recovery: Line drawing analysis, stereo, shading, motion, texture. Use of object models for prediction and recognition. Also listed as Vision Science C280.
Sections Instructor Teaching Effectiveness How worthwhile was this course?
Spring 2016 Trevor Darrell 4.9 / 7 5.5 / 7
Alexei Efros 6.3 / 7 5.8 / 7
Spring 2015 Jitendra Malik 6.2 / 7 6.1 / 7
Fall 2013 Alexei Efros 6.0 / 7 6.0 / 7
Jitendra Malik 6.0 / 7 5.9 / 7
Fall 2012 Jitendra Malik 6.4 / 7 6.8 / 7
Spring 2012 Jitendra Malik 6.5 / 7 6.3 / 7
Fall 2010 Jitendra Malik 6.3 / 7 6.2 / 7
Fall 2009 Trevor Darrell 5.5 / 7 5.9 / 7
Fall 2008 Jitendra Malik 6.2 / 7 6.0 / 7
Fall 2007 Jitendra Malik 6.2 / 7 5.8 / 7
Spring 2005 Christopher Geyer 3.9 / 7 4.4 / 7
Fall 2003 David Forsyth 5.9 / 7 5.8 / 7
Fall 2002 Berthold Horn 6.5 / 7 6.2 / 7
Spring 2002 David Forsyth 5.9 / 7 5.6 / 7
Spring 2001 David Forsyth 5.6 / 7 5.3 / 7
Spring 2000 David Forsyth 5.4 / 7 4.6 / 7
Spring 1999 Jitendra Malik 6.2 / 7 5.9 / 7
Fall 1997 David Forsyth 4.7 / 7 4.6 / 7
Spring 1997 J. Weber 6.1 / 7 5.7 / 7
Spring 1996 Jitendra Malik 5.9 / 7 6.0 / 7
Spring 1995 Jitendra Malik 5.9 / 7 5.8 / 7
Spring 1994 Jitendra Malik 6.2 / 7 6.1 / 7
Spring 1993 Jitendra Malik 5.4 / 7 5.3 / 7
Spring 1992 Jitendra Malik 5.7 / 7 5.5 / 7
Fall 1989 Jitendra Malik 5.7 / 7 4.9 / 7
Spring 1989 Jitendra Malik 5.3 / 7 5.1 / 7
Overall Rating Teaching Effectiveness How worthwhile was this course?
5.8 / 7 5.7 / 7
[Email HKN about this data] [Info about this page]