# Computational Vision

## CSCI-4270

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
Prereqs:
CSCI-2300 Introduction To Algorithms
and
one of:
MATH-4100 Linear Algebra CSCI-2210 Math Fndtns Of Machine Lrning

# Past Term Data
Offered
Not Offered
Offered as Cross-Listing Only
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| Spring | Summer | Fall
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| (Session 1) | (Session 2) |
2025 | Computational Vision (4c) Charles V Stewart Seats Taken: 0/100 |  |
2024 | Computational Vision (4c) Charles V Stewart Seats Taken: 92/100 |  |
2023 |  |  |
2022 | Computational Vision (4c) Charles V Stewart Seats Taken: 60/70 |  |
2021 | Computational Vision (4c) Charles V Stewart Seats Taken: 43/100 |  |
2020 |  |  |
2019 |  |  | Computational Vision (4c) Charles V Stewart Seats Taken: 56/66
2018 |  |  | Computational Vision (4c) Charles V Stewart Seats Taken: 65/66
2017 |  |  | Computational Vision (4c) Charles V Stewart Seats Taken: 28/40
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