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PILAB Perceptual Intelligence Laboratory
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Computer Vision

CMPE 537

Instructor: Prof. Lale Akarun

Credits: 3

Course Description

Graduate computer vision: imaging and cameras, filtering and CNNs, segmentation, features, object and action recognition, motion, tracking, and 3D vision—with implementations and a term project.

Tentative topics

  1. Introduction, applications, course requirements
  2. Human visual system, color coordinate systems, radiometry
  3. Imaging geometry, camera calibration, image formation, imaging devices
  4. Basic image processing operations; edge detection
  5. Filters; image pyramids; CNNs
  6. Segmentation: boundary-based and region-based
  7. Representation: feature extraction; feature learning
  8. Object detection and recognition; DNN-based techniques
  9. DNN-based video action recognition
  10. Motion and tracking
  11. 3D vision

Textbook

Forsyth, D. A., & Ponce, J. (2011). Computer Vision: A Modern Approach (2nd ed.). Prentice Hall.

Term project

There is a term project (including a written component). Students prepare a survey related to the project topic and give a short presentation by the fourth week of the semester. They then propose an implementation or simulation, carry it through the term, and present in the last week of the semester.

Homework

Homework assignments are computer implementations of algorithms discussed in class. Expect roughly 3–4 assignments.