Abstract
Human beings are hard-wired to discover and categorize patterns as a way of understanding the world. Humans can easily read poorly handwritten text, parse and understand human speech, recognize faces in a crowd, classify people as old or young, etc. However, it is difficult for a machine to solve these kinds of perceptual problems. In this chapter, we explore some of the concepts and techniques of machine-based pattern recognition, including statistical-based approaches and neural network-based approaches. We illustrate the concepts with case studies taken from image and video processing.
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© 2012 Springer-Verlag London Ltd.
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Shridhar, M., Watta, P. (2012). Pattern Recognition. In: Batchelor, B.G. (eds) Machine Vision Handbook. Springer, London. https://doi.org/10.1007/978-1-84996-169-1_24
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DOI: https://doi.org/10.1007/978-1-84996-169-1_24
Publisher Name: Springer, London
Print ISBN: 978-1-84996-168-4
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