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020 _a9789812870568
_9978-981-287-056-8
024 7 _a10.1007/978-981-287-056-8
_2doi
035 _ato000546823
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aPisharady, Pramod Kumar.
_eauthor.
_9456317
245 1 0 _aComputational Intelligence in Multi-Feature Visual Pattern Recognition
_helectronic resource
_bHand Posture and Face Recognition using Biologically Inspired Approaches /
_cby Pramod Kumar Pisharady, Prahlad Vadakkepat, Loh Ai Poh.
260 _aSingapore :
_bSpringer Singapore :
_bImprint: Springer,
_c2014.
300 _aXIII, 138 p. 50 illus., 25 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v556
505 0 _aPart I Computational Intelligence in Visual Pattern Recognition -- 1 Visual Pattern Recognition -- 2 Computational Intelligence Techniques -- 3 Multi-Feature Pattern Recognition -- Part II Feature Selection and Classification -- 4 Fuzzy-Rough Discriminative Feature Selection and Classification -- 5 Hand Posture and Face Recognition using Fuzzy-Rough Approach -- 6 Boosting based Fuzzy-Rough Pattern Classifier -- Part III Biologically Inspired Approaches in Hand Posture Recognition -- 7 Hand Posture Recognition using Neurobiologically Inspired Features -- 8 Attention based Segmentation and Recognition (ASR) Algorithm for Hand Postures Against Complex Backgrounds -- Appendices -- Index.
520 _aThis 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.
650 0 _aengineering.
_9224332
650 0 _aOptical pattern recognition.
_9304126
650 0 _aAlgorithms.
_9304813
650 1 4 _aEngineering.
_9224332
650 2 4 _aComputational Intelligence.
_9307538
650 2 4 _aPattern Recognition.
_9304129
650 2 4 _aAlgorithms.
_9304813
700 1 _aVadakkepat, Prahlad.
_eauthor.
_9417560
700 1 _aPoh, Loh Ai.
_eauthor.
_9456318
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
830 0 _aStudies in Computational Intelligence,
_9305181
856 4 0 _uhttp://dx.doi.org/10.1007/978-981-287-056-8
912 _aZDB-2-ENG
999 _c404696