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E-book
Author Rahman, S. M. Mahbubur, author

Title Orthogonal image moments for human-centric visual pattern recognition / S.M. Mahbubur Rahman, Tamanna Howlader, Dimitrios Hatzinakos
Published Singapore : Springer, 2019

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Description 1 online resource (xii, 149 pages) : illustrations (some color)
Series Cognitive Intelligence and Robotics, 2520-1956
Cognitive intelligence and robotics, 2520-1956
Contents Intro; Preface; Contents; About the Authors; 1 Introduction; 1.1 Introduction; 1.2 Pattern Recognition: Mimicking the Human Visual System; 1.3 Human-Centric Visual Patterns; 1.4 Features for Visual Pattern Recognition; 1.5 Moments as Features; 1.6 Outline of the Book; References; 2 Image Moments and Moment Invariants; 2.1 Introduction; 2.2 Geometric Moments; 2.2.1 Preliminaries of Geometric Moments; 2.2.2 Discrete Implementation of Geometric Moments; 2.2.3 Geometric Moments and Fourier Transform; 2.3 Orthogonal Moments; 2.3.1 Gaussian-Hermite Moments; 2.3.2 Krawtchouk Moments
2.3.3 Tchebichef Moments2.3.4 Zernike Moments; 2.4 Invariance Properties of 2D Moments; 2.4.1 Translation Invariants; 2.4.2 Scaling Invariants; 2.4.3 Rotation Invariants; 2.4.4 Invariants of Orthogonal Moments; 2.5 Conclusion; References; 3 Face Recognition; 3.1 Introduction; 3.2 What is Face Recognition?; 3.3 Facial Features: A Brief Review; 3.4 Moments as Facial Features; 3.5 Discriminative Selection of Moments; 3.5.1 ICC-Based Selection of Moments; 3.5.2 Fisher Scoring of Moments; 3.5.3 AVR-Based Selection of Moments; 3.5.4 Discriminative Features from Moments
3.6 Classification of Discriminative Features3.6.1 Naive Bayes Classifier; 3.6.2 Quadratic Discriminant Classifier; 3.6.3 Nearest Neighbor Classifier; 3.7 Experiments on Moment-Based Face Recognition; 3.7.1 Face Databases; 3.7.2 DGHMs for Appearance-Type Recognition; 3.7.3 DKCMs for Hybrid-Type Recognition; 3.7.4 DGHMs for Recognition in SSS Case; 3.8 Conclusion; References; 4 Expression Recognition; 4.1 Introduction; 4.2 Related Works on Facial Expression Analysis; 4.3 Representation of Facial Expressions Using Moments; 4.4 Discriminative Versus Differential Components of Moments
4.5 Moment-Based Features for Facial Expressions4.5.1 Discriminative Selection of Moments; 4.5.2 Differential Components for Moment-Based Features; 4.5.3 Expressive Moment-Based Feature Vector; 4.6 Feature Classification; 4.7 Overview of Moment-Based FER System; 4.8 Experimental Results; 4.8.1 Expression Databases; 4.8.2 Experimental Setup; 4.8.3 Performance Evaluation; 4.9 Conclusion; References; 5 Fingerprint Classification; 5.1 Introduction; 5.2 Related Works; 5.3 Moments and Singular Points; 5.4 Extraction of Singular Points; 5.5 Classification of Fingerprints; 5.6 Experimental Results
5.7 ConclusionReferences; 6 Iris Recognition; 6.1 Introduction; 6.2 Iris Template from Eye Images; 6.2.1 Eye in Constrained Setting; 6.2.2 Eye in Unconstrained Setting; 6.2.3 Rectangular Iris Template; 6.3 Binary Features for IrisCode; 6.3.1 Moment-Based IrisCodes; 6.4 Discriminative Masking of IrisCode; 6.5 Verification Performance of IrisCode; 6.6 Experimental Results; 6.7 Conclusion; References; 7 Conclusion; 7.1 Summary of Moment-Based Visual Pattern Recognition; 7.2 Future Directions on Moment-Based Pattern Recognition; References
Summary Instead of focusing on the mathematical properties of moments, this book is a compendium of research that demonstrates the effectiveness of orthogonal moment-based features in face recognition, expression recognition, fingerprint recognition and iris recognition. The usefulness of moments and their invariants in pattern recognition is well known. What is less well known is how orthogonal moments may be applied to specific problems in human-centric visual pattern recognition. Unlike previous books, this work highlights the fundamental issues involved in moment-based pattern recognition, from the selection of discriminative features in a high-dimensional setting, to addressing the question of how to classify a large number of patterns based on small training samples. In addition to offering new concepts that illustrate the use of statistical methods in addressing some of these issues, the book presents recent results and provides guidance on implementing the methods. Accordingly, it will be of interest to researchers and graduate students working in the broad areas of computer vision and visual pattern recognition
Bibliography Includes bibliographical references
Notes Online resource; title from PDF title page (SpringerLink, viewed October 14, 2019)
Subject Pattern recognition systems.
Computer vision.
Pattern Recognition, Automated
Computer vision
Pattern recognition systems
Form Electronic book
Author Howlader, Tamanna, author
Hatzinakos, Dimitrios, author.
ISBN 9789813299450
9813299452