Overview

This text explores the viability of the application of High-order synthetic neural network technology to transformation-invariant recognition of complex visual patterns. High-order networks require little training data (hence, short training times) and have been used to perform transformation-invariant recognition of relatively simple visual patterns, achieving very high recognition rates. The successful results of these methods provided inspiration to address more practical problems which have grayscale as opposed to binary patterns (for example, alphanumeric characters, aircraft silhouettes), and are also more complex in nature as opposed to purely edge-extracted images - human face recognition is such a problem. This text serves as a reference for researchers and professionals working on applying neural network technology to the recognition of complex visual patterns.

Full Product Details

Author:   Okechukwu A. Uwechue ,  Abhijit S. Pandya
Publisher:   Springer
Imprint:   Springer
Edition:   1997 ed.
Volume:   410
Dimensions:   Width: 15.50cm , Height: 0.90cm , Length: 23.50cm
Weight:   0.840kg
ISBN:  

9780792399575

ISBN 10:   0792399579
Pages:   123
Publication Date:   30 June 1997
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

1. Introduction.- 1.1 Objective.- 1.2 Background to Neural Networks.- 1.3 Organization of book.- 2. Face Recognition.- 2.1 Background.- 2.2 Various methods.- 2.3 Neural Net Approach.- 3. Implementation of Invariances.- 3.1 Matching of similar triplets.- 3.2 Software implementation.- 4. Simple Pattern Recognition.- 4.1 Procedure.- 4.2 Results.- 5. Facial Pattern Recognition.- 5.1 Two-dimensional moment invariants.- 5.2 Face Segmentation.- 5.3 Isodensity regions.- 5.4 Reducing sensitivity to lighting conditions.- 5.5 Image encoding algorithm.- 5.6 The use of gradient images.- 6. Network Training.- 6.1 Training algorithms.- 6.2 Modifications to training algorithms.- 6.3 Training image data.- 6.4 Results.- 7. Conclusions amp; Contributions 111.- 8. Future Work.- 8.1 Simultaneous Training on all four Isodensity Images.- 8.2 Higher-resolution coarse image size.- 8.3 Automatic face recognition.- 8.4 MIMO third-order networks.- 8.5 Zernike and Complex moments.- 8.6 Recognition of facial expressions (moods).- Index 119.

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