Resources

A large number of websites contain information of relevance to this course, including the following.

• The 4422/5323 Course Homepage provides information regarding matters particular to this course.
• The Center for Vision Research Homepage provides information about research in computer and biological vision that is done at York University (get involved)!<!-- * The [[http://www.cs.cmu.edu/afs/cs/project/cil/www/vision.html|Computer Vision Homepage]] is a valuable resource for more general information regarding computer vision.-->
• The Vision Science Homepage provides more general information about biological vision.

Of the many books on computer vision and related topics, the following may be of particular use to students of this course (in addition to the course textbook).

• V. Bruce, P. Green and M. Georgeson, Visual Perception: Physiology, Psychology and Ecology, Psychology Press, 1998.
• R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, Wiley-Interscience, 2001.
• B. K. P. Horn, Robot Vision, MIT Press, Cambridge, MA, 1996.
• B. Jahne and H. Hausbecker (Eds.) Handbook of Computer Vision and Applications, Springer, Berlin, 2000.
• J. Lim, Two-dimensional Signal and Image Processing, McGraw-Hill, NY, NY, 1995.
• P. Wolf, Elements of Photogrammetry, Mcgraw-Hill, 2000.

Journals that regularly contain contributions from computer vision include the following.

• IEEE Transactions on Pattern Analysis and Machine Intelligence
• International Journal of Computer Vision
• Computer Vision and Image Understanding (and previous incarnations, e.g., CVGIP, etc.)
• Image and Vision Computing
• IEEE Transactions on Image Processing

Conferences that regularly contain contributions from computer vision include the following (all of these conferences publish proceedings).

• IEEE International Conference on Computer Vision
• European Conference on Computer Vision
• IEEE Conference on Computer Vision and Pattern Recognition
• IAPR International Conference on Pattern Recognition
• IEEE International Conference on Image Processing

The recommended programming languages for use in this course are Python or C++.

• OpenCV provides a powerful open-source tool for computer vision development, and it is a good tool to gain experience with. There are many tutorials which can be found online showcasing the use of OpenCV.
• Note that although Jupyter Notebooks are popular tools with a lot of advantages, they are problematic for code release (see Joel Grus' slides on the topic). Any projects developed in Python should be available outside of Jupyter Notebooks.

It is recommended that written reports for this course be prepared in LaTeX, which is a commonly used documentation preparation system in computer science.

• For those new to LaTeX or who prefer a more graphical interface, LyX can be a helpful and easy to use front-end.