EE544

Computer Vision

Motivation:

The focus of this module is to produce graduates with a deeper theoretical understanding of the issues that underpin computer vision. It will build on the basic framework laid down in EE425/EE453 (or equivalent) with a view to delving deeper into to some of the topics introduced in previous modules. In addition it will introduce a range of advanced techniques and methodologies current in computer vision research. This module is primarily aimed at those who aim to undertake research in computer vision or require a deeper understanding of the subject to address commercial computer vision development. A significant element of the module will focus on developing independent learning skills for computer vision research. While we will not cover the prerequisite material in class, the course manual / textbook will contain the majority of material for review by the student if required. Additional information on this module can be found on the "Prospective Students" section of the main DCU web site.

Indicative Content:

  • Introduction, Prerequisite Review (Examples)
  • Noise Reduction Techniques (1 & 2)
  • Mathematical Morphology (2 & 3; Applications)
  • Metrics & Image Classification (2)
  • Colour Image Processing & Analysis (2)
  • Texture Analysis (2 & 3)
  • Eigenimage analysis
  • Active Contours / Meshes / Models
  • 3D Vision: Stereo vision / Depth from Defocus / Depth from focusing / Triangulation and laser scanning / Applications of 3D sensors to industrial processes
  • Recent computer vision research

Indicative Reading List:

  • Paul F Whelan, Online course long form notes (including self assessment questions) and class notes (slides)
  • Milan Sonka, Vaclav Hlavac, Roger Boyle, Image Processing: Analysis and Machine Vision
  • Pierre Soille, Morphological Image Analysis: Principles and Applications
  • Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision
  • Research papers from leading computer vision journals.
  • On-line Internet teaching resources.
Course Access
 

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