Digital Image Processing And Pattern Recognition

Digital Image Processing And Pattern Recognition
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Digital Image Processing And Pattern Recognition

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Publisher: PHI Learning
ISBN: 9788120340916
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Availability: In Stock
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About The Book Digital Image Processing And Pattern Recognition
Book Summary:

This book is designed for undergraduate and postgraduate students of Computer Science and Engineering, Information Technology, Electronics and Communication Engineering, and Electrical Engineering.

The book comprehensively covers all the important topics in digital image processing and pattern recognition along with the fundamental concepts, mathematical preliminaries and theoretical derivations of significant theorems. The image processing topics include coverage of image formation, digitization, lower level processing, image analysis, image compression, and so on. The topics on pattern recognition include statistical decision making, decision tree learning, artificial neural networks, clustering and others. An application of simulated annealing for edge detection is described in an appendix. The book is profusely illustrated with more than 200 figures and sketches as an added feature.

KEY FEATURES :

Provides a large number of worked examples to strengthen the grasp of the concepts.

Lays considerable emphasis on the algorithms in order to teach students how to write good practical programs for problem solving.

Devotes a separate chapter to currently used image format standards.

Offers problems at the end of each chapter to help students test their understanding of the fundamentals of the subject.


Table of Contents:
Preface
1. INTRODUCTION
2. IMAGE ACQUISITION
3. SAMPLING AND DIGITIZATION
4. FUNDAMENTALS OF DIGITAL IMAGES
5. IMAGE TRANSFORMS
6. IMAGE ENHANCEMENT
7. COLOUR IMAGE PROCESSING
8. IMAGE RESTORATION
9. IMAGE REGISTRATION
10. EDGE DETECTION
11. IMAGE SEGMENTATION
12. IMAGE COMPRESSION
13. IMAGE FILE FORMATS
14. FEATURE EXTRACTION AND REPRESENTATION
15. PATTERN RECOGNITION
16. CLASSIFICATION AND DECISION MAKING
17. STATISTICAL DECISION MAKING
18. NEAREST NEIGHBOUR CLASSIFIER
19. DECISION TREE LEARNING
20. RECOGNITION AND ARTIFICIAL NEURAL NETWORKS
21. CLUSTERING
Appendix: Edge Detection using Simulated Annealing
Index