| |
Dec 21, 2024
|
|
|
|
|
CS 4830 - Computer Vision2 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 algorithm 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 or MA 2314 and 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
Dr. Josiah Yoder
Add to Portfolio (opens a new window)
|
|
Skip to Main Content
