CSE 166: Image Processing

Fall 2017

Instructor: Ben Ochoa
Email: bochoa at
Office hours: M 5:00 PM-6:00 PM, EBU3B 4208, and at other times by appointment

TA: Rithwik Kollipara
Email: rkollipa at
Office hours: Th 3:00 PM-4:00 PM, EBU3B B260A, and F 3:00 PM-4:00 PM, EBU3B B240A.

Tutor: Ashwin Srikant
Email: asrikant at
Office hours: Tu 5:00 PM-6:00 PM, EBU3B B250A

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

Class section ID: 915217
Lecture: MW 6:30 PM-7:50 PM, CENTR 119
Discussion: Tu 8:00 PM-8:50 PM, CENTR 119
Class discussion: Piazza

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: Assignments will have a submission procedure described with the assignment. Assignments submitted late will receive a 15% grade reduction for each 12 hours late (i.e., 30% per day). Assignments will not be accepted 72 hours after the due date. If you require an extension (for personal reasons only) to a due date, you must request one as far in advance as possible. Extensions requested close to or after the due date will only be granted for clear emergencies or clearly unforeseeable circumstances. You are advised to begin working on assignments as soon as they are assigned.

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%


Lecture slides:

Lecture topics (tentative):

Required textbook:

Digital Image Processing, 4th edition
Rafael C. Gonzalez and Richard E. Woods
Pearson, 2018

Last update: December 5, 2017