CS6850 Image Understanding

Homework assignment notes/hints page

A Style Guide is available on-line to assist you in determining the correct style for your programs. You are required to follow the guidelines in all programs you turn in for the course. Failure to follow the guidelines may result in a significantly lower grade on an assignment.

Animated TeacherInstructor: David M. Chelberg (Press here to email)flying letter!
Class Number 13497
Office: Stocker 322B
Office Hours: Mon, Wed, Fri 3pm-4pm and by email appointment. Feel free to just stop by as well.
Lecture Notes
will be available from prime in the directory: ~cs685/lectures. They are stored as compressed postscript files. To print, you must first uncompress using the command gunzip, then send to a postscript printer (using lp). Detailed instructions for those new to Unix.

To provide students with a comprehensive overview of the principles of image understanding. To become acquainted with many current techniques in image understanding.

Proficiency in C++, data structures and algorithms, and mathematical proficiency.
Required Texts:
Computer Vision, by Linda G. Shapiro, and George C. Stockman, Prentice-Hall, Upper Saddle River, New Jersey, 2001. ISBN 0-13-030796-3.

Course Outline:
Topics include:
Students are expected to spend a substantial amount of time outside of class working exercises in the book, and programming homework problems.
Examination schedule:
There will be two midterm exams approximately weeks 5 and 10. The final exam is replaced by a final project. Final project presentations will take place at the time/date of the final.
Attendance Policy:
Students are strongly encouraged to attend all classes, but attendance is not required. Class attendance will not be used in the final determination of grades. Students miss classes at their own risk. Students are required to attend class during the midterm and final exam unless prior arrangements have been made.
Academic dishonesty:
Students are expected to turn in only their own work with proper documentation. Anything else will result in an F for the exam, project or program, and possibly an F for the course, or even dismissal from the University. This means NO WORKING IN GROUPS (unless expressly allowed), and NO SHARING CODE.
Sensor Animation
CCD vs CMOS explained with animations
Medial Axis Animation
Dinosaurs, and medial axis computation
Code Resources
OpenCV Library Sourceforge Project
Morphology Links
Grayscale Morphology with Demo
Hypermedia Image Processing Java Code demos
CVonline: Morphological Transformations
Robotics Links
Flex picker robots -- very fast
Misc Resources
Otsu's method for histogram based thresholding.
Lecture notes on histograms from Prof Emmanuel Agu, Worcester Polytechnic Institute.
Image moments, including how to calculate the orientation of a region from the moments.
Eigen values and vectors of a 2x2 matrix.
Supercomputer Vision
Facial Recognition Tested
Computational Photography
The computer vision homepage (lots of good links)
USC Bibliography of Computer Vision
A good free on-line text book of computer vision.
CV Online General Overview of Computer Vision with lots of resources.
Ballard and Brown Computer Vision Text on-line (classic computer vision book)
Intelligence: The Eye, the Brain and the Computer, M. A. Fischler, O. Firschein, Addison-Wesley, 1987, ISBN: 0201120011
3D Model Recognition From Stereoscopic Cues, J.E. W. Mayhew, J.P. Frisby; MIT Press, 1991, ISBN 0-262-13243-5
A good review of probability for Image Understanding/Computer Vision purposes.
A good site with an overview of the Canny edge detector, and an interactive demo.
Stanford University Class Note site (good source for camera calibration lecture)
Canny's Edge Detection Paper 1986 (from TPAMI).
Alternate site for Canny's Edge Detection Paper 1986 (from TPAMI).
Good video about tristimulus theory of color, but funny, and other random stuff as well.

David M. Chelberg <chelberg@ohiou.edu>
last-modified: Thu Oct 23 11:33:49 2014