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.

Part 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.

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.

Comprehensive coverage of various steps issues in visual pattern recognition for young researchers and students Contains focused discussion on hand gesture recognition for experienced researchers and scientists Includes algorithms that could be extended to other pattern recognition tasks like face and object recognition The block diagrams and pseudo codes provided help better understanding of the algorithms presented The tables with performance comparisons help the reader to make the right choice of the algorithm for a certain application Includes supplementary material: sn.pub/extras

GPSR Compliance The European Union's (EU) General Product Safety Regulation (GPSR) is a set of rules that requires consumer products to be safe and our obligations to ensure this. If you have any concerns about our products you can contact us on ProductSafety@springernature.com. In case Publisher is established outside the EU, the EU authorized representative is: Springer Nature Customer Service Center GmbH Europaplatz 3 69115 Heidelberg, Germany ProductSafety@springernature.com