Course CSE327
Title Fundamentals of Computer Vision
Credits 3
Course Coordinator

Dimitris Samaras


Introduces fundamental concepts, algorithms, and techniques in visual information processing. Covers image formation, binary image processing, image features, model fitting, optics, illumination, texture, motion, segmentation, and object recognition.

Prerequisite CSE 214 or CSE 230 or CSE 260; AMS 210 or MAT 211
Course Outcomes
  • Working knowledge of fundamental concepts and computational techniques in visual information processing.
  • An ability to design algorithms for understanding images and video, such as segmentation, edge detection, and reflectance analysis.


Computer Vision: Algorithms & Applications by Richard Szeliski; Springer (ISBN # 978-1848829343)

Major Topics Covered in Course
  • Computational algorithms and their implementation for the following applications: automatic inspection and measurement based on binary image processing (two-dimensional machine vision).
  • Gray-level image processing and analysis techniques (e.g. image segmentation, edge detection, image filtering, curve fitting).
  • Three-dimensional shape recovery through stereo image analysis.
  • Object recognition based on feature vector classification and template matching.

Laboratory Projects
  • Image processing: mask-based filtering, median filtering, subsampling, edge-detection (2 weeks)
  • Feature detection, Morphing and Mosaicing images (2 weeks)
  • Stereo Reconstruction (2 weeks)
  • Final project. Select from suggested topcs (4 weeks)

Course Webpage