Computer Science

Computer Vision

Computer Vision
Introduction to fundamental concepts and algorithms of computer vision, including feature extraction, detection, segmentation, registration, recognition, motion, 3D vision, and image understanding. Applies techniques from image processing, geometry, Bayesian methods, optimization, and machine learning to computer vision problems.
 Hours3.0 Credit, 3.0 Lecture, 0.0 Lab
 PrerequisitesC S 312 & MATH 313; or C S 312 & MATH 213 & MATH 215
 RecommendedStat 121 or Stat 201.
 NoteStudents are allowed 1 repeat of each C S undergraduate course (all 100-, 200-, 300- or 400-level courses). This includes all students who received any grade including those who withdraw (receive a "W" grade) from a C S course. Students must wait 1 semester/term before being allowed to take a course they have failed twice. Petitions for exceptions to the policy can be completed at
 ProgramsContaining C S 450
Course Outcomes: 

Introduction to Digital Signal and Image Processing

Understand and apply the following:

  • Sampling and quantization of image information
  • Camera properties and the acquisition of images
  • Mathematics of image-space transformations
  • Fourier transforms and filtering