Computational Vision

CSCI-6270

The goal of this course is to introduce students to the problems, challenges, and applications of computer vision from a computational perspective. Topics include camera modeling and image formation, feature extraction, object and face recognition, image mosaic construction, stereo and three-dimensional imaging, motion, and tracking. Machine learning methods, including deep convolutional neural networks, will be studied and applied throughout the course.

4 credits
Cross-listed with:
Prereqs:
none

Past Term Data

Offered
Not Offered
Offered as Cross-Listing Only
No Term Data
Spring Summer Fall
(Session 1) (Session 2)
2023
2022
Computational Vision (4c)
  • Charles V Stewart
Seats Taken: 17/20
2021
Computational Vision (4c)
  • Charles V Stewart
Seats Taken: 15/20
2020
2019
Computational Vision (4c)
  • Charles V Stewart
Seats Taken: 19/24
2018
Computational Vision (4c)
  • Charles V Stewart
Seats Taken: 15/21
2017
Computational Vision (3c)
  • Charles V Stewart
Seats Taken: 22/40
2016
2015
Computational Vision (3c)
  • Charles V Stewart
Seats Taken: 9/30
2014
2013
2012
Computational Vision (3c)
  • Charles V Stewart
Seats Taken: 14/42
2011
Computational Vision (3c)
  • Pamela Paslow
  • Charles V Stewart
Seats Taken: 8/42
2010
Computational Vision (3c)
  • Charles V Stewart
Seats Taken: 13/30
2009
Computational Vision (3c)
  • Charles V Stewart
Seats Taken: 15/30
2008
2007