DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

UNIVERSITY OF CALIFORNIA, SAN DIEGO

Instructor: Ben Ochoa

Email: bochoa at ucsd.edu

Office hours: W 8:00 PM-9:00 PM, EBU3B 3208

TA: Vrinda Gupta

Email: vrg001 at eng.ucsd.edu

Office hours: M 9:00 AM-10:00 AM, EBU3B B250A, and Th 2:30 PM-3:30 PM, EBU3B B240A.

Tutor: Dhanesh Pradhan

Email: dpradhan at eng.ucsd.edu

Note: when emailing the instructor or TA with questions about the class, please put "CSE 166" in the subject line.

Class section ID: 882388

Lecture: MW 6:30 PM-7:50 PM, CENTR 105

Discussion: F 12:00 noon-12:50 PM, CSB 002

Class discussion: Piazza

Grades: GradeSource

This course covers the foundational topics of digital image processing, including acquisition, filtering, enhancement, restoration, color image processing, multiresolution image representations, compression, morphological image processing, and segmentation.

Prerequisites: Linear algebra and calculus; and data structures.

Programming aspects of the assignments will be completed using MATLAB.

Late Policy: Written homework will be due in class and accepted thereafter with a penalty of 10% per day starting from the due date. Programming assignments will have a hand-in procedure described with the assignment, and also has a 10% per day late penalty. No assignments will be accepted after the graded assignments have been returned or the solutions have been released.

Academic Integrity Policy: Integrity of scholarship is essential for an academic community. The University expects that both faculty and students will honor this principle and in so doing protect the validity of University intellectual work. For students, this means that all academic work will be done by the individual to whom it is assigned, without unauthorized aid of any kind.

Collaboration Policy: It is expected that you complete your academic assignments on your own (or in your own group) and in your own words and code. The assignments have been developed by the instructor to facilitate your learning and to provide a method for fairly evaluating your knowledge and abilities (not the knowledge and abilities of others). So, to facilitate learning, you are authorized to discuss assignments with others; however, to ensure fair evaluations, you are not authorized to use the answers developed by another, copy the work completed by others in the past or present, or write your academic assignments in collaboration with another person. If the work you submit is determined to be other than your own, you will be reported to the Academic Integrity Office for violating UCSD's Policy on Integrity of Scholarship.

Grading: There will be homework assignments, a midterm exam, and a final exam weighted with the following percentages:

Assignments: 50%

Midterm exam: 20%

Final exam: 30%

Assignments:

- Assignment 1 (due Oct 5) data
- Assignment 2 (due Oct 12) data
- Assignment 3 (due Oct 24)
- Assignment 4 (due Nov 16)
- Assignment 5 (due Nov 30)

Handouts/Readings:

- Course overview (from Sep 26 lecture)
- Midterm review (from Oct 26 lecture)
- Final review (from Nov 30 lecture)

Lecture topics (tentative):

- Introduction to image processing
- Image acquisition, geometric transformations, and image interpolation, Chapter 2
- Intensity transformations, Chapter 3
- Spatial filtering, Chapter 3
- The continuous Fourier transform, Chapter 4
- Sampling and aliasing, and the discrete Fourier transform, Chapter 4
- Filtering in the frequency domain, Chapter 4
- Image restoration, Chapter 5
- Color image processing, Chapter 6
- Image pyramids and subband coding, Chapter 7
- Multiresolution expansions, Chapter 7
- The wavelet transform, Chapter 7
- Image compression, Chapter 8
- Image watermarking, Chapter 8
- Morphological image processing, Chapter 9
- Morphological algorithms, Chapter 9
- Image segmentation, Chapter 10

Required textbook:

Digital Image Processing, 3rd edition

Rafael C. Gonzalez and Richard E. Woods

Pearson, 2008

[Amazon]

*Last update: November 30, 2016*