CSC 162 Computer Vision

Lecture: 2 Lab: 3 Clinic: 0 Credits: 3

This course provides an introduction to the fundamentals of computer vision. Topics include image classification, motion tracking, imaging geometry, image formation, feature detection, feature matching, classical machine learning, and deep learning. Upon completion, students should be able to apply computer vision design and technologies in various applications.

Course is typically offered in Spring.
Student Learning Outcomes (SLOs)
At the completion of the course, students should be able to do the following:

Solve computer vision problems using feature detection, feature matching, and imaging geometry processing techniques.
Design image classification and motion tracking systems with both classical machine learning and deep learning algorithms.
Evaluate computer vision technologies that address challenges in diverse fields with computer vision design principles.