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:

CSCI-4270 Computational Vision

Prereqs:

none

Past Term Data

Offered

Not Offered

Offered as Cross-Listing Only

No Term Data

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	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	Computational Vision (3c) Daniel Freedman Seats Taken: 10/30
2006	Computational Vision (3c) Daniel Freedman Seats Taken: 6/30
2005
2004	Computational Vision (3c) Daniel Freedman Seats Taken: 8/30			Computational Vision (3c) Daniel Freedman Seats Taken: 7/30
2003	Computational Vision (3c) Daniel Freedman Seats Taken: 11/40
2002	Computational Vision (3c) Daniel Freedman Seats Taken: 11/40
2001	Computational Vision (3c) Daniel Freedman Seats Taken: 13/40
2000
1999				Computational Vision (3c) Charles V Stewart Seats Taken: 11/50
1998