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Module Descriptor School of Computer Science and Statistics

Module CodeCS7008
Module NameVision Systems
Module Short Title
ECTS5
Semester Taught1st semester
Contact Hours

2 lectures and 1 tutorial per week

Module PersonnelDr. Mukta Prasad
Learning Outcomes

When students have successfully completed this module they should be able to:

  • design solutions to real-world problems using computer vision.
  • develop working computer vision systems.
  • critically appraise computer vision techniques.
  • explain, compare and contrast computer vision techniques.
Learning Aims

This course on computer vision will aim to teach students basics with a special emphasis on image-based computer vision drawing on principles from optics, signal processing, machine learning and algorithms. The students will use their learning to design solutions on popular categories of vision problems related to entertainment, surveillance and other such image based tasks. Additionally, the students will also learn to implement such solutions in practice.

Module Content

Delivery:                                     

The teaching strategy employed on this course is a mixture of lectures and problem-solving tutorials/laboratories. The laboratory assignments allow the students to appreciate the difficulties of actually realising real solutions using computer vision. The tutorials allow the students to develop a better understanding of the material and to practise the design of appropriate solutions. Students make use of OpenCV/MATLAB, to experiment with many of the computer vision techniques, and to implement their assignments.

 

Syllabus:

Specific topics addressed in this module include:

  • Image processing and feature extraction
  • Shape representations and transformations
  • Machine learning and inference for machine vision: regression, classification, retrieval,
Recommended Reading List
  • Computer Vision: Models, learning and inference (free pdf at http://www.computervisionmodels.com/ )
  • Computer Vision: Algorithms and Applications (free pdf at http://szeliski.org/Book/)
  • “A Practical Introduction to Computer Vision with OpenCV”  (http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1118848454.html)
  • Learning OpenCV, Gary Bradski & Adrian Kaehler, O’Reilly, 2008.
Module Prerequisites

A working knowledge of C++/ matlab

Assessment Details

The labs and assignments account for 60% of the final mark and the exam 40%.   Students must answer 2 out of 3 exam questions.

Supplemental Assessment: An assignment followed by a report and a presentation for 100% (to be completed by exam boards)

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