Computer Vision, CIS581, Fall 2008

Monday/Wednesday 4:30pm-6:00pm, Towne 309

Instructor: Jianbo Shi, GRW 466

TA: Mack White

This is an introduction course to computer vision, modeled after a similar course at CMU called “Computational Photography”.  This course will explore three topics: 1) image morphing, 2) shape matching, and 3) image search.  This course is intended to provide you a hands-on experience with interesting things to do on images/pixels.

The world is becoming image-centric.  Camera are now found everywhere, in our cell phones, automobiles, even in medical surgery tools. Computer vision technology has lead to latest innovations in areas such as Hollywood movie production, medical diagnosis, biometrics, and digital library.  

This course is suited for students with all Engineering background, who has the basic knowledge of linear algebra and programming, and a lot of imagination.


Grading Policy: 3 homeworks/projects 60%, Midterm 20%, Final Project 20%.

Project 3 , Homograph code is here.

Recommended Textbook:

  1. Computer Vision a Modern Approach, Forsyth and Ponce, Prentice Hall, 2003. (a complete textbook on computer vision)

  1. Vision Science: Photons to Phenomenology, Stephen Palmer. (a great book to read)

Background knowledge required: Linear Algebra, Basic programming skill.

Related Web pages:

CSE 399b, Spring 2005 by Kostas Daniilidis: http://www.seas.upenn.edu/~cse399b/SP05/

A similar class at CMU with great slides by Alyosha Efors: http://graphics.cs.cmu.edu/courses/15-463/2004_fall/www/463.html

A fun to watch DVD on “Computer Vision, Fact & Fiction” by Serge Belongie: http://vision.ucsd.edu/cvd/

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Class Schedule

  

9/3

Introduction: pixels

  

9/8

Image Formation, Camera Note PDF,

9/10

Image Feature: filtering Note PDF,

9/15

Guest Lecture, Color

9/17

Matlab Tutorial

9/22

Image Feature: edge detection homework 1

9/24

9/29

Image Geometry: image warping Notes

10/1

Image Geometry: mesh Note 1 Note 2

10/6

Image Geometry: mesh,

10/8

Image Geometry: TPS warping Note Project 2

10/15

Project 1 Presentation

10/20

Image Feature: image pyramid Note, Paper

10/22

Image Blending Note.

 

10/27

Image Geometric Features, SIFT Note

 

10/29

Image Features: RANSAC Note

 

11/3

Geometric Features: image mosaic.

11/5

In class midterm

11/10

Recogntion, Introduction Notes

11/12

Recognition: Shape Context Note

11/17

Shape recognition Note , Paper.

11/19

Recognition: Pictorial Objects. paper

 

11/24

Recognition: Shape Context.

11/26

Face Detection.

12/1

Recognition: Bag of features.

12/3

Recognition: Segmentation Notes