Computer Vision Course

How to Use this Material

The material on these webpages is intended to provide an introduction to Computer Vision and is to be used as pre-course material for a set of in-depth lectures. In order to get the most out of the lectures it is important that you understand the terminology and the basic operation of the algorithms described.

For each course unit, follow the terminology list as a guide to find out about and understand the algorithms identified. This material is intended as a guide and not as a complete set of notes, you should refer to the resources as a starting point for detailed material (this resource includes book and web references as well as some locally stored material).

You will need to attempt the practicals (Labs) associated with each unit in the order in which they appear as results from each project are used in subsequent assesed projects. Details of course assessment are provided for your information together with the dates for each submission of work.

The time allocation at the top of each unit and lab exercise should be used only as a guide. Use the mini questions (MCQ's) related to each unit and move on when you feel you can answer them reliably. Finally, "Terminology" are provided as examples of the level of understanding required to answer examination questions.

Course Units

Course Unit	Labs
Image Representation	Image Manipulation in MATLAB
Simple Image Processing	Histogram Modification
Frequency Domain and Convolution	Deconvolution
Image Noise/Corruption	Noise and Fourier Transforms
Types of Linear Filters	Filter Design
Rank Filters	Segmentation and Noise Filtering
Region Segmentation	Threshold-based Segmentation (Assessed)
Edge and Corner Detection	Edge Detection
Probability and Statistics	No practical
Pattern Recognition	Neural Networks

(c) Imaging Science and Biomedical Engineering 2000 [paul.bromiley@man.ac.uk]