Module Descriptor School of Computer Science and Statistics
|Module Name||Vision Systems|
|Module Short Title|
|Semester Taught||1st semester|
2 lectures and 1 tutorial per week
|Module Personnel||Dr. Mukta Prasad|
When students have successfully completed this module they should be able to:
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.
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.
Specific topics addressed in this module include:
|Recommended Reading List|
A working knowledge of C++/ matlab
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)
|Academic Year of Data|