Qiang Ji - Rensselaer Polytechnic Institute
Genre: course
3D Computer Vision
http://www.ecse.rpi.edu/Homepages/qji/CV/ecse6650_syllabus.html

This course deals with the science and engineering of computer vision, that is, the analysis of patterns in visual images of a 3D scene with the goal of interpreting, understanding, and reconstructing the 3D scene. The emphasis is on physical, mathematical, and information processing aspects of vision. Topics to be covered include image formation and representation, feature extraction, camera calibration, image noise representation and propagation, reconstruction of depth based on stereo, shading, focus, texture, and geometry, motion analysis, analytical performance characterization, and analysis and recognition of objects and scenes using statistical and model-based techniques. This course will be very useful for students interested in robotics, photogrammetry, remote sensing, and medical imaging.

Martin Jagersand - University of Alberta
Genre: course
3-Dimensional Computer Vision
http://www.cs.ualberta.ca/~jag/courses/CompVis/

This course is on 3D computer vision, which focusses on how to make use of the spatial and temporal coherence imposed by camera geometry to reconstruct a 3D geometric model. The course will devote roughly equal time to the following four subtopics: 1. Preliminaries: Mathematics and image processing 2. Geometric vision 3. Dynamic vision 4. Applications

Octavia Camps - Pennsylvania State University
Genre: course
Advanced Topics in Computer Vision
http://www.cse.psu.edu/~cg586

Graduate course in computer vision, crosslisted between the EE and CSE departments. Significant part of the course is dedicated to the discussion of articles recently published in the literature.

Angel Sanchez - ESCET - URJC, Spain
Genre: course
Computational Vision
http://gavab.escet.urjc.es/index_en.html

The course offers an introduction to the related problems and techniques of image processing/computer vision from an algorithmic and practical perspective. It is the integration of algorithm design techniques and example cases from industry into the course.

Robert Fisher - University of Edinburgh
Genre: course
Computational Vision
http://www.dai.ed.ac.uk/dai/teaching/modules/cv/

The aims of this module are: (a) to provide a general introduction to the field of computer vision; (b) to provide practical experience with some vision techniques; (c) to encourage and assist critical thinking about scientific theories of vision, perception and cognition. The course covers low level vision (feature detectors, neurophysiology, Marr's Primal Sketch), mid-level vision (stereo, optical flow, shape-from-shading, Marr's 2.5D sketch, active vision) and high level vision (ACRONYM, Marr, visual agnosias, geometric invariance).

Ellen Walker - Hiram College
Genre: course
Computer Vision
http://cs.hiram.edu/~walkerel/cs320

Computer vision in the context of building an object recognition system. A general overview, with emphasis on what will make their projects work You could say, their projects were the focus.

Roger Boyle - University of Leeds, UK
Genre: course
Computer Vision
http://www.comp.leeds.ac.uk/cgi-bin/sis/ext/handbook_pub.cgi?module=COMP3300&year=2003&cmd=displayentry&pagetype=soc&non_comp=0

The focus of the course is on local research, including video monitoring and 3D medical imaging

Kevin Bowyer - University of South Florida
Genre: course
Computer Vision


An introduction to computer vision techniques.

Greg Hager - Johns Hopkins University
Genre: course
Computer Vision
http://www.ugrad.cs.jhu.edu/~cs461/

An introductory course covering basic techniques on image processing, segmentation, reconstruction, and recognition.

Scott Umbaugh - Southern Illinois University at Edwardsville
Genre: course
Computer Vision
http://www.ee.siue.edu/~sumbaug/438_syl.html

To introduce the student to computer vision algorithms, methods and concepts which will enable the student to implement computer vision systems with emphasis on applications and problem solving. Lab exercises will familiarize the student with typical hardware as well as software development tools. Students will use the C programming language to implement computer vision algorithms.