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Computational Intelligence in Multi-Feature Visual Pattern Recognition

Hand Posture and Face Recognition using Biologically Inspired Approaches

  • Pramod Kumar Pisharady
  • Prahlad Vadakkepat
  • Loh Ai Poh

Part of the Studies in Computational Intelligence book series (SCI, volume 556)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Computational Intelligence in Visual Pattern Recognition

    1. Front Matter
      Pages 1-1
    2. Pramod Kumar Pisharady, Prahlad Vadakkepat, Loh Ai Poh
      Pages 3-9
    3. Pramod Kumar Pisharady, Prahlad Vadakkepat, Loh Ai Poh
      Pages 11-19
    4. Pramod Kumar Pisharady, Prahlad Vadakkepat, Loh Ai Poh
      Pages 21-37
  3. Feature Selection and Classification

    1. Front Matter
      Pages 39-39
    2. Pramod Kumar Pisharady, Prahlad Vadakkepat, Loh Ai Poh
      Pages 41-61
    3. Pramod Kumar Pisharady, Prahlad Vadakkepat, Loh Ai Poh
      Pages 63-80
    4. Pramod Kumar Pisharady, Prahlad Vadakkepat, Loh Ai Poh
      Pages 81-92
  4. Biologically Inspired Approaches in Hand Posture Recognition

    1. Front Matter
      Pages 93-93
    2. Pramod Kumar Pisharady, Prahlad Vadakkepat, Loh Ai Poh
      Pages 95-106
    3. Pramod Kumar Pisharady, Prahlad Vadakkepat, Loh Ai Poh
      Pages 107-131
  5. Back Matter
    Pages 133-138

About this book

Introduction

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.

Keywords

Artificial Intelligence Face Recognition Feature Extraction Algorithms Fuzzy-Rough Approach Gesture Recognition Hand Posture Recognition Machine Learning Visual Pattern Recognition

Authors and affiliations

  • Pramod Kumar Pisharady
    • 1
  • Prahlad Vadakkepat
    • 2
  • Loh Ai Poh
    • 3
  1. 1.Inst. of High Performance Computing, A*STARSingaporeSingapore
  2. 2.Electrical and Computer EngineeringNational University of SingaporeSingaporeSingapore
  3. 3.Electrical and Computer EngineeringNational University of SingaporeSingaporeSingapore

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-287-056-8
  • Copyright Information Springer Science+Business Media Singapore 2014
  • Publisher Name Springer, Singapore
  • eBook Packages Engineering
  • Print ISBN 978-981-287-055-1
  • Online ISBN 978-981-287-056-8
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site