198:534   Computer Vision

Fall 2007

 

 

Class Materials Page

 

• Lectures
• Readings
• Homework Assignments
• Resources

 

Lectures:

 

Date		Lecture
9/5/07	Week 1	1- Introduction
9/12/07	Week 2	2- Human Vision 3- Cameras and Lenses
9/19/07	Week 3	4- Binary Image Analysis
9/23/07	Week 4	6- Linear Filters
10/3/07	Week 5	7- Edge Detection 8- Texture
10/10/07	Week 6	9- Camera Geometry
10/17/07		no class - ICCV
10/24/07	Week 7	10 - Camera Calibration
10/31/07	Week 8	11 - Stereo imaging and Multiple View Geometry, active stereo
11/7/07	Week 9	12- Perceptual Grouping and Segmentation by Clustering   13- Segmentation- Graph cuts
11/14/07	Week 10	14- Markov Random Fields and their applications to segmentation and stereo.  Stereo imaging (Cont.) - Markov Random Fields
11/21/07	Week 11	Midterm   15- Segmentation- statistical methods, mean shift
11/28/07	Week 12	- Model Fitting and segmentation - 3D Model-based Recognition and Pose Recovery   - Appearance-based Vision
12/5/07	Week 13	19- Object Detection and Recognition
12/12/07	Week 14	20- Tracking

 

Sample Midterm Exam Question

 

Reading materials:

 

• David H. Hubel & Torsten N. Wiesel “Brain Mechanisms of Vision” Scientific American, 1979.
• [required reading] Jianbo Shi and Jitendra Malik “Normalized Cuts and Image Segmetnation” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 22 No. 0, August 2000
• [required reading] D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603--619, 2002.
• Yuri Boykov, Olga Veksler, Ramin Zabih "Fast Approximate Energy Minimization via Graph Cuts". In IEEE transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 23, no. 11, pp. 1222-1239, 2001.

 

Homework Assignments:

 

• Assignment 1: Binary Image Analysis for Hand-written isolated character recognition.
• Assignment 2: Nonparametric Texture Synthesis
• Assignment 3: Camera Calibration
• Assignment 4: Image Segmentation using Normalized Cuts

  

 

 

General Useful Computer Vision Resources:

 

§        Computer Vision Home Page (CMU): http://www-cgi.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html

 

§        CV online: http://www.dai.ed.ac.uk/CVonline/

 

§        Keith Price Annotated Computer Vision Bibliography at USC:

http://iris.usc.edu/Vision-Notes/bibliography/contents.html

 

§        Open CV: Intel Open Source CV library.

http://www.intel.com/research/mrl/research/opencv/

 

§        Microsoft Vision SDK http://research.microsoft.com/projects/VisSDK/

 

§        Matlab Image Processing toolbox:

http://www.mathworks.com/access/helpdesk/help/toolbox/images/images.shtml

 

 

 

 

 Matlab Resources

 

q       The most popular Matlab tutorial is the one written by Kermit Sigmon of University of Florida:

 

Matlab Tutorial by Prof Sigmon

 

q       An extensive tutorial from Mathworks

                       

 

q       Online resources for Matlab Tutorial are:

 

1.     http://math.ucsd.edu/~driver/21d-s99/matlab-primer.html

2.     http://www4.ncsu.edu/unity/users/p/pfackler/www/MPRIMER.htm

3.     http://www.glue.umd.edu/~nsw/ench250/primer.htm

4.     http://www.math.ukans.edu/docs/matlab/matlab-primer.html

5.     http://www.math.ufl.edu/help/matlab-tutorial/

6.    http://www-personal.engin.umich.edu/~tilbury/tutorials/matlab_tutorial.html

 

q       Some  resources for image processing using Matlab follows:

 

1.     http://www.cnb.uam.es/~coss/ImageProcessing/Tutorial/

2.     http://www.csee.wvu.edu/~trapp/wvumatlab.htm

3.     http://www.mathworks.com/products/image/description1.jsp

4.     http://amath.colorado.edu/courses/4720/2000Spr/Labs/Worksheets/Matlab_tutorial/matlabimpr.html