CSE 152: Introduction to Computer Vision

Spring 2017

Instructor: Ben Ochoa
Email: bochoa at
Office hours: M 8:00 PM-9:00 PM, EBU3B 3208

TA: Lenord Melvix
Email: lmelvix at
Office hours: M 12:00 noon-2:00 PM, EBU3B B240A

TA: Jean Choi
Email: jsc078 at
Office hours: W 12:00 noon-2:00 PM, EBU3B B240A

TA: Daniel Peroni
Email: dperoni at
Office hours: Tu 1:00 PM-2:00 PM, EBU3B B250A

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

Class section ID: 903774
Lecture: MW 5:00 PM-6:20 PM, CENTR 105
Discussion: Th 8:00 PM-8:50 PM, HSS 1330 (optional)
Class discussion: Piazza

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 or Python.

Late Policy: No assignments will be accepted after the due date.

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.



Lecture slides:

Lecture topics (tentative):

Optional textbook:

Computer Vision: A Modern Approach, 2nd edition
David A. Forsyth and Jean Ponce
Pearson, 2011

Other helpful textbooks:

Computer Vision: Algorithms and Applications
Richard Szeliski
Springer, 2011
[Amazon] [Google]
Introductory Techniques for 3-D Computer Vision
Emanuele Trucco and Alessandro Verri
Prentice Hall, 1998

Multiple View Geometry in Computer Vision, 2nd edition
Richard Hartley and Andrew Zisserman
Cambridge University Press, 2004
[Cambridge Books Online] [Amazon] [Google]
Digital Image Processing, 3rd edition
Rafael C. Gonzalez and Richard E. Woods
Pearson, 2008

Last update: June 6, 2017