# 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
Cross-listed with:
CSCI-6270 Computational Vision
Prereqs:
CSCI-2300 Introduction To Algorithms
and
one of:
CSCI-2210 Math Fndtns Of Machine Lrning MATH-2010 Multivar Calc & Matrix Algebra

# Past Term Data
Offered
Not Offered
Offered as Cross-Listing Only
No Term Data
Simple View
Detailed View

| Spring | Summer | Fall
--- | --- | --- | ---
| (Session 1) | (Session 2) |

2026

Computational Vision (4c)

- Charles V Stewart

Seats Taken: 106/150

2025

Computational Vision (4c)

- Charles V Stewart

Seats Taken: 94/120

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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