Course Lecturers: Dr. Aphrodite Galata and Dr. Carole Twining
Demonstrators: Peter Thomson and Crefeda Rodrigues
IntroductionThis unit will give students a foundation in the subject of machine vision. This will involve gaining familiarity with algorithms for low-level and intermediate-level processing and considering the organisation of practical systems. Particular emphasis will be placed on the importance of representation in making explicit prior knowledge, control strategy and interpretting hypotheses.
This course unit treats vision as a process of inference from noisy and uncertain data and emphasizes probabilistic and statistical approaches. As such, it will also give students a foundation in the statistical methods of image analysis.
Topics covered in the course include perception of 3D scene structure from stereo; image filtering, smoothing, edge detection; segmentation and grouping; learning, recognition, and search; tracking and motion estimation; behaviour modelling.
Is this course right for you?This course unit is designed for students that are interested in Computer Vision, Artificial Intelligence, or Machine Learning. This course unit is also appropriate for students with an interest in Computer Graphics.
The labs will make extensive use of Matlab.