The application of concepts from chaos theory to a broad range of lower
level computer vision has proven useful throughout the text so far. These tasks have
included motion detection and segmentation, texture analysis and image registration and
tracking. The application of chaos theory to the higher level computer vision task of
pattern recognition has also been an area of active research. This is particularly true in
the area of chaotic neural networks based on the known chaotic behavior of biological
neural systems. Pattern recognition can be considered an optimization problem where
the best matching pattern maximizes the probability of correct classification. The
application of optimization algorithms such as genetic algorithms to pattern recognition
has also proven fruitful, and the dependence of these algorithms on random number
generation for processes such as mutation makes them logical candidates for
improvements using chaos theory to develop more robust random behavior. In this
chapter the application of chaos theory to neural networks and genetic algorithms for
the high level computer vision function of object recognition will be explored.
Keywords: Classification, pattern recognition, optimization, neural networks,
genetic algorithms, chaotic mutation, chaotic neural networks, chaotic genetic
algorithms.
