Computer image processing and pattern recognition
R A Jarvis and D Suter
3 points * 16 lectures, 9 laboratory hours, extensive reading * Second semester * Clayton * Prerequisites: Level 2 substantially complete
Objectives The student is expected to acquire an understanding of the related topics of image processing and pattern recognition and a critical judgement concerning the appropriate application scope of the methodologies covered both through the lectures and wider reading of available materials.
Synopsis Image processing: image formation and discretisation, pixel point operators, spatial operators - convolutions and non-linear operators, edge detection and segmentation, computer implementation. Pattern recognition: binary vector component decision rules, Bayesian minimal risk formulations, sequential pattern recognition, linguistic pattern recognition, fuzzy logic and stochastic production systems, minimal spanning trees, clustering methodologies, neural networks, application scope.
Assessment Examination (2 hours): 60% * Assignments: 40%
Recommended texts
Gonzales R C and Wintz P Digital image processing 2nd edn, Addison-Wesley, 1987
Jain A K Fundamentals of digital image processing Prentice-Hall, 1989
| Published by Monash University, Clayton, Victoria
3168 Copyright © Monash University 1996 - All Rights Reserved - Caution Authorised by the Academic Registrar December 1996 |