CSE 152: Introduction to Computer Vision

Spring 2015

Instructor: Ben Ochoa
Email: bochoa at
Office hours: Tu 6:30 PM-7:30 PM

TA: Akshat Dave
Email: akdave at
Office hours: MW 6:00 PM-7:00 PM, EBU3B 4152

Note: when emailing the instructor or TA with questions about the class, please put "CSE 152" in the subject line.

Class section ID: 840039
Lecture: TuTh 5:00 PM-6:20 PM, CSB 005
Discussion: F 11:00 AM-11:50 AM, WLH 2207 (optional)
Class discussion: Piazza
Grades: GradeSource

The goal of computer vision is to compute properties of the three-dimensional world from images and video. Problems in this field include indentifying the 3D shape of a scene, determining how things are moving, and recognizing familiar people and objects. This course provides an introduction to computer vision, including such topics as feature detection, image segmentation, motion estimation, object recognition, and 3D shape reconstruction through stereo, photometric stereo, and structure from motion.

Prerequisites: Linear algebra and calculus; data structures/algorithms; and MATLAB, Python, C/C++, or other programming experience.

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: 55%
Midterm exam: 15% (sample questions)
Final exam: 30%


Lecture slides:

Lecture topics (tentative):

Required textbook:

Computer Vision: Algorithms and Applications
Richard Szeliski
Springer, 2011
[Amazon] [Google]

Other helpful textbooks:

Introductory Techniques for 3-D Computer Vision
Emanuele Trucco and Alessandro Verri
Prentice Hall, 1998
(textbook for CSE 152 in previous years, now out of print)

Computer Vision: A Modern Approach, 2nd edition
David A. Forsyth and Jean Ponce
Prentice Hall, 2011
(textbook for CSE 252A)

Multiple View Geometry in Computer Vision, 2nd edition
Richard Hartley and Andrew Zisserman
Cambridge University Press, 2004
[Cambridge Books Online] [Amazon] [Google]
(textbook for CSE 252B)

Last update: June 4, 2015