DIGITAL IMAGE PROCESSING

Announcements

2009-01-03

Welcome to the web site for Digital Image Processing. This web site will act as a conduit for lecture notes and problems sets, throughout the semester, other materials will be posted on Courseworks.

 

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 3:00 - 4:00 pm, 1312 Mudd
Teaching assistant:	Graham Grindlay Email: grindlay at ee dot columbia dot edu Office hour: Thursdays 2:00-4:00pm Location: CEPSR 714 (7th floor lounge) Tel: 212-854-0235 (LabROSA) Mailbox: J5 (in Mudd 13th Floor EE Student room)
	Textbook: Digital Image Processing, 3rd Edition by Gonzalez and Woods, Prentice Hall 2008 (ISBN 9780131687288) links on amazon, bn 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. Sonka, Hlavac and Boyle, Image Processing, Analysis, and Machine Vision, 3rd edition,CENGAGE-Engineering
Lectures:	Mondays, 4:10 - 6:40 pm Mudd 1127 CU directory link
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, six homeworks (practical and experimental), a midterm and a final. The grade breakdown will be announced.

HWs will include both written problems and practical problems that will be run under the numerical computation package Matlab, or other computer language of your choice.

HWs are due Mondays 4:10pm in class in TA's mailbox or email inbox.

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): 0.8
2-4 days (25-96 hrs): 0.5
up to a week (7 days, or 148 rs) late: 0.2
after that 0.0

Course materials

Lecture notes, HWs and other course materials will be posted either on the course outline page of this web site or ColumbiaCourseworks.

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.