 | Undergraduate Course Descriptions
CSE152 - Introduction to Computer Vision
Units: 4
Course Objectives:
This course would form a three course sequence in Artificial
Intelligence along with CSE 150 and CSE 151. These two other classes
are being redesigned right now, but they will cover core AI topics such
as logical inference, heuristic search, planning, probabilistic inference,
and learning. CSE152 will focus on machine perception, particularly
computer vision. All three courses can be taken independently of each other,
in any order. There is no overlap in material coverage. CSE 152 will also
serve as part of a potential undergraduate graphics and vision sequence.
It will complement the image processing course (CSE166), with very minimal
overlap (perhaps 10%). While CSE 166 covers 2-D processing of images
(including image enhancement, restoration, and segmentation, stochastic
image models, Filter design, sampling, compression Fourier and wavelet transforms),
CSE 152 will cover inference of 3-D properties from 2-D images (including
motion understanding, stereo, structure from motion, photometric stereo)
and object recognition.
Course Description:
The goal of computer vision is to compute properties of the
three-demensional world from images and video. Problems inthis field
include indentifying the 3D shape of a scene, determining how things
are moving, and recongnizing familiar people and objects. This course
provides an introduction to computer vision, including such topics as
feature dectection, image segmentation, motion estimation, object recognition,
and 3D shape reconstruction through stereo, photometric stereo, and structure
from motion.
Format:
3 hours of lecture per week, 1 hours per week of discussion section,
4 hours of lab per week, and 4 hours of outside preparation.
Prerequisites:
Math 20F, CSE 100 or Math 176, CSE 101 or Math 188.
Knowledge of C, C++ or Matlab programming.
Other restrictions:
Majors only.
Laboratory work:
Programming assignments in MATlab.
Offered:
One quarter per year, Spring.
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