CS 4830 - Computer Vision

2 lecture hours 2 lab hours 3 credits

Course Description
This class provides a survey of modern computer vision topics and a computer vision design experience. After a brief introduction to the array representation of images and classical low-level algorithms, this course lays the foundation for modern computer vision on the foundational concepts of camera geometry, feature extraction, and machine learning. Students will implement a modern computer vision algorithm in a series of structured labs, after which they will implement a computer vision algorithms in a project experience. This class is intended for students with a strong programming background. (prereq: Junior standing in CE or SE program, MA 231, MA 383 or instructor consent)

Course Learning Outcomes
Upon successful completion of this course, the student will be able to:

Interpret gray-scale and color images encoded as Matlab arrays
Implement simple computer vision algorithms by operating on raw pixel values
Compute projections and back-projections using the pinhole camera model
Stitch panoramas using homographies and RANSAC
Interpret machine learning algorithms as partitions of multi-dimensional space
Implement features and describe their role in vision
Understand the value of real-world and synthetic testing for computer vision algorithms
Design and implement a computer vision algorithm

Prerequisites by Topic

Matrix multiplication
Eigenvectors/Eigenvalues
Procedural programming
Partial derivatives

Coordinator
Josiah Yoder

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