DIGITAL IMAGE PROCESSING

Announcements

2007-01-10

Welcome to the web site for Digital Image Processing. Below is some introductory material about the class. This web site will act as the main conduit for lecture notes, problems sets, etc. throughout the semester.

2007-01-22

Lecture notes and background reading for today's lecture are now available at the course outline page. The course mechanics sheet is here.

General Information

Lecturers:	Lexing Xie Research Staff Member IBM T J Watson Research Center xlx _at_ ee.columbia.edu Tel 914-784-6191	Shahram Ebadollahi Research Staff Member IBM T J Watson Research Center shahram _at_ ee.columbia.edu Tel 914-784-6250
Instructor office hours:	Mondays 5:30-6:30pm, 1312 Mudd
Teaching assistant:	Junfeng He jh2700 _at_ columbia.edu Tel: 212 854 6887 Office hours: Fridays 2:00-4:00pm, CEPSR 711 Mailbox: #K4, Student Lounge, 13th Floor Mudd
Text:	Textbook: Digital Image Processing, 2nd Edition by Gonzalez and Woods, Prentice Hall 2002 (ISBN 0201180758) Reference books: Fundamentals of Digital Image Processing, Anil K. Jain, Prentice Hall, 1989  William K. Pratt, Digital Image Processing, 3rd Edition, John Wiley, 2001. Kenneth R. Castleman, Digital Image Processing, Prentice Hall, 1996. Arun N. Netravali, Barry G. Haskell, Digital Pictures, Plenum, 2e, 1995.
Lectures:	Mondays, 6:50 - 9:20pm Room 337 Mudd
Credits:	3
Course web site:	http://www.ee.columbia.edu/~xlx/ee4830/

Overview

This course will introduce fundamental technologies for digital image and video representation, compression, analysis, and processing. Students will gain understanding of algorithm and system design, analytical tools, and practical implementations of various digital image applications.

Topics include digital image/video perception, sampling, optimal quantization, halftoning, transform, filtering, multi-spectral processing, restoration, analysis, feature extraction, morphological transform, coding, segmentation, and 3D model reconstruction. Considerations of practical system requirements (e.g., medical, satellite, consumer) will be discussed as well. We will also have hands on experience in applying analytical solutions in practical applications.

Prerequisites

The course assumes knowledge about signals and systems, as well as a basic familarity of linear algebra and probability.

Grade structure

The course consists of lectures each week, twelve homeworks (eight analytical, four experimental), a midterm (2.5 hrs) and a final (3 hrs). The grade will be broken down as follows:

HW	Analytical	25%
	Experimental	25%
Mid-term		20%
Final		30%

Late policy for homeworks, applying to both the analyticals and experimentals. 

You have an option to submit the HWs beyond the due date (but before the solutions are out), they will be graded with the same standard but will be multiplied by the following damping factors (as a function of time) when it counts towards the overall grade.

1 day (1-24 hrs): 0.9
2-4 days (25-96 hrs): 0.6
up to a week (7 days, or 148 rs) late: 0.3
after that 0.0

Course materials

Lecture notes, HWs and other course materials will be announced on the course outline page of this web site directly.
HWs are due by the end of the day of classes (Mondays 11:59pm) in TA's mailbox or email inbox. Late problems sets will not be accepted.

Half of the HWs will include practical problem that will be run under the numerical computation package Matlab, or other computer language of your choice.

Communications

Important announcements and class updates will be sent to the class either via email and/or the course webpage. Please check both regularly.

Please put "[DIP-E4830]" in the title of any email correspondences you send with the instructors or the TA. Thank you.