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This book presents a collection of computational intelligence
algorithms that addresses issues in visual pattern recognition such
as high computational complexity, abundance of pattern features,
sensitivity to size and shape variations and poor performance
against complex backgrounds. The book has 3 parts. Part 1 describes
various research issues in the field with a survey of the related
literature. Part 2 presents computational intelligence based
algorithms for feature selection and classification. The algorithms
are discriminative and fast. The main application area considered
is hand posture recognition. The book also discusses utility of
these algorithms in other visual as well as non-visual pattern
recognition tasks including face recognition, general object
recognition and cancer / tumor classification. Part 3 presents
biologically inspired algorithms for feature extraction. The visual
cortex model based features discussed have invariance with respect
to appearance and size of the hand, and provide good inter class
discrimination. A Bayesian model of visual attention is described
which is effective in handling complex background problem in hand
posture recognition. The book provides qualitative and quantitative
performance comparisons for the algorithms outlined, with other
standard methods in machine learning and computer vision. The book
is self-contained with several figures, charts, tables and
equations helping the reader to understand the material presented
without instruction.
This book presents a collection of computational intelligence
algorithms that addresses issues in visual pattern recognition such
as high computational complexity, abundance of pattern features,
sensitivity to size and shape variations and poor performance
against complex backgrounds. The book has 3 parts. Part 1 describes
various research issues in the field with a survey of the related
literature. Part 2 presents computational intelligence based
algorithms for feature selection and classification. The algorithms
are discriminative and fast. The main application area considered
is hand posture recognition. The book also discusses utility of
these algorithms in other visual as well as non-visual pattern
recognition tasks including face recognition, general object
recognition and cancer / tumor classification. Part 3 presents
biologically inspired algorithms for feature extraction. The visual
cortex model based features discussed have invariance with respect
to appearance and size of the hand, and provide good inter class
discrimination. A Bayesian model of visual attention is described
which is effective in handling complex background problem in hand
posture recognition. The book provides qualitative and quantitative
performance comparisons for the algorithms outlined, with other
standard methods in machine learning and computer vision. The book
is self-contained with several figures, charts, tables and
equations helping the reader to understand the material presented
without instruction.
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