EE520: Special Topics in Comm. & Signal
Processing, Fall 2005
(Signal Processing for Image Analysis and Computer Vision)
The course is about answering the following question: Given an image, a set of images or a time sequence of images (video) what can you infer about the scene? "Inference" includes both detection problems such as object recognition (or more generally pattern recognition) or image retrieval, and estimation problems in both 2D and 3D. 2D estimation includes segmentation, registration and tracking in 2D, while 3D estimation includes reconstruction of the 3D scene, which may be static (stereo) or moving (structure from motion). In addition we will also spend some time on shape analysis, shape matching and shape tracking. "Image Analysis" is a generic term which refers to all of the above, while "Computer Vision" usually refers to deducing the properties of the 3D world from one or multiple images.
Prerequisites - Basic signal processing, probability/statistics, calculus. Estimation and Detection theory may be useful
Instructor - Dr. Namrata Vaswani, Email: namrata AT iastate.edu, Phone: 515-294-4012, Office: 3121 Coover Hall
Class Time and Room: Monday-Wednesday,12pm - 1:20pm in 1207 Coover Hall
Office Hours: 3121 Coover Hall, Mondays, 2 - 3:30pm or any other time b/w 2-7pm on Mondays and Wednesdays (call to make sure I'm in) or any other day.
Discussion Session: 5:15 - 6pm Mondays in Sproul (2202 Coover): Plan to discuss (i) questions related to what's taught in class, (ii) MATLAB or general implementation issues not addressed in class, (iii) problems from your research, or (iv) projects. Those auditing are also encouraged to come and participate.
If anyone shows up, please call me or come to my office.
Midterm Exam: October 17 (Monday)
Topics: Edge detection, Calculus of Variations, Optical Flow, Segmentation, Registration, Least Squares, Tracking
Everything that is on the handouts and sections of books/papers that any handout asks you to read
Project Deadlines, Details Please start deciding your projects!
(NEW) Homework 4
Handouts: (These may have errors/typos, please please email me if you find any)
Some Scanned Notes: (read at your own risk! Alternate pages are inverted, print carefully)
Reading Material: These are only very small subset of the many papers or books on any topic.
Edge Detection - Chapter 9 of A.K. Jain's book, Section 4.3 of Sonka et al's book
Texture - Section 14.1 of Sonka et al, Manjunath, & Chellappa's paper
Optical Flow - Chapter 5 of Tekalp's book, Chapter 14 of Sonka et al's book, Weblink
Segmentation & Registration
Sections 5.1-5.3, Section 8.2 of Sonka et al's book, Chapter 1 of Sapiro's book,
Kass,Witkin,Terzopolous, IJCV 1987 (Parametric)
Kichenassamy et al ICCV 1995 ("Geometric" Edge-based)
Yezzi, Tsai, Wilsky, LIDS Technical Report, 1999 ("Geometric" Region-based)
Chan,Vese, Trans IP 2001 ("Geometric" Region-based)
Level Set Method:
Publications page (specifically see the Narrowband Level Set
Methods and Extension Velocities papers)
Statistical Shape Analysis: Dryden & Mardia's book, Chapter 2, 2.1-2.2, Chapter 3, 3.1-3.4, Chapter 4, 4.1, 4.3
Kalman & Extended Kalman Filter:
EKF for SfM - Broida & Chellappa
Multiple Hypothesis Trackers
Hartley & Zisserman, Chapter 6, 6.1 - 6.4
Epipolar Geometry: Hartley & Zisserman, Chapter 9, 9.1 - 9.5
Reconstruction of Cameras & Structure: Hartley & Zisserman, Chapter 10, 10.1 - 10.3
Estimating the Fundamental Matrix: Hartley & Zisserman, Chapter 11.1 - 11.4.1
Hartley & Zisserman, Chapter 12.1 - 12.4
Old talk with too much detail: Particle Filtering and Change Detection
Other Related Course pages (with lots of useful handouts and links):
EE527 (Detection and Estimation Theory)
STAT580 (Computational Methods on Statistics) - see for handout on Monte Carlo methods
EE524 (Digital Signal Processing)
EE523 (Random Processes for Communications and Signal Processing)