CE 261: Digital Image Processing

handouts location: http://www.cse.ucsc.edu/classes/cmpe261
Social Sciences II, Room # 137
Monday, Wednesday, Friday 9:30-10:40 AM
Office hours: Monday, Wednesday, Friday 11:00-12:00 AM

Prerequisites: Graduate standing or permission of instructor.

Text: Digital Image Processing by R.C. Gonzales and R. E. Woods, 1992.

Optional Text: Digital Image Processing by K. Castleman, Prentice Hall, 1996.

Requirements: 7 homeworks (10%) and 4 laboratory assignments (30%), 2 exams (30%), comprehensive final (30%).

The course is to have two exams to be in class and a final. Homeworks and small programming projects will be assigned.

Week [1] of April 1:  Image processing, (Ch. 1 & Ch. 2.1-2.4)
  
Week [2] of April 8:     Point processing (Ch. 4.1 & 4.2) HW #1 Lab #1
  
Week [3] of April 15:    Windowed processing (Convolutions, Morphology, 
 	distance transform) (Ch. 4.3 & Ch. 8.4) HW #2

Week [4] of April 22:    Image transforms (Fast Fourier transform, 
  Discrete cosine transform) (Ch. 3 & Ch. 4.4, 4.5) HW #3 Lab #2

Week [5] of April 29:    More image transforms Test 1. 
  
Week [6] of May 6:    Image processing applications HW #4

Week [7] of May 13:   Data structures, coding, & compression (Ch. 6) HW #5
  
Week [8] of May 20:   Segmentation (Ch. 7) HW #6 Lab #3
  
Week [9] of May 28(Exchange day):   Recognition (Ch. 9) HW #7
  
Week [10] of June 3:   Parallel Image Computing  
 (Papers/Leighton/Tanimoto) Lab #4

 Final Exam, Tuesday June 11, 4:00 - 7:00 pm

Reference material:

Optional Text: Digital Image Processing by K. Castleman, Prentice Hall, 1996.

Encyclopedia of Graphics File Formats by Murray and vanRyper, 1994

Multidimensional Digital Signal Processing, by Dudgeon and Mersereau, 1984.

Computer Graphics, Principles and Practice, Second Edition, by Foley, vanDam, Feiner, and Hughes, 1990.

Fundamentals of Digital Image Processing, by Anil K. Jain, 1989.

Digital Image Processing, by K. R. Castleman, 1979.

Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes by F. T. Leighton, 1992.

ImageVision Library (TM) Programming Guide, by Neider and Bassler, Silicon Graphics, 1993.

Course Description:

261. Digital Image Processing. Spring. Topics in digital image processing and display: image processing, computer vision, visual perception, digital representation, transformations, sampling, enhancement, restoration, coding and compression, parallel input and output hardware/software systems. Emphasis on algorithms, coding, and implementation of computer image processing software. Additional material will include mid-level vision, high-level vision, and parallel algo rithms and hardware for digital image processing. The course will introduce students to image processing packages though hands on use, development libraries, and programming hardware accelerated Silicon Graphics workstations through C/C++ application program ming. Readings and lectures supplemented by homework, lab exercises, and projects.

Policies:

All course work including homework, programs and exams are intended as individual effort and are graded as such. It's okay to discuss general approaches and algorithms with other students, but this should be done without sharing code. Cheating or plagiarism in any form will not be tolerated. Punishment will match severity of offense. You are responsible for protecting your homework solutions and programs from being copied by others. Do not discard printouts in public places. And don't forget to logout.