UID:
edocfu_9959233116702883
Format:
1 online resource (407 p.)
ISBN:
1-281-03838-5
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9786611038380
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0-08-055144-0
Series Statement:
Neural network systems, techniques, and applications ; v. 5
Content:
Image Processing and Pattern Recognition covers major applications in the field, including optical character recognition, speech classification, medical imaging, paper currency recognition, classification reliability techniques, and sensor technology. The text emphasizes algorithms and architectures for achieving practical and effective systems, and presents many examples. Practitioners, researchers, and students in computer science, electrical engineering, andradiology, as well as those working at financial institutions, will value this unique and authoritative reference to diverse app
Note:
Description based upon print version of record.
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Front Cover; Image Processing and Pattern Recognition; Copyright Page; Contents; Contributors; Preface; Chapter 1. Pattern Recognition; I. Introduction; II. Pattern Recognition Problem; III. Neural Networks in Feature Extraction; IV. Classification Methods: Statistical and Neural; V. Neural Network Applications in Pattern Recognition; VI. Summary; References; Chapter 2. Comparison of Statistical and Neural Classifiers and Their Applications to Optical Character Recognition and Speech Classification; I. Introduction; II. Applications; III. Data Acquisition and Preprocessing
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IV. Statistical ClassifiersV. Neural Classifiers; VI. Literature Survey; VII. Simulation Results; VIII. Conclusions; References; Chapter 3. Medical Imaging; I. Introduction; II. Review of Artificial Neural Network Applications in Medical Imaging; III. Segmentation of Arteriograms; IV. Back-Propagation Artificial Neural Network for Arteriogram Segmentation: A Supervised Approach; V. Self-Adaptive Artificial Neural Network for Arteriogram Segmentation: An Unsupervised Approach; VI. Conclusions; References; Chapter 4. Paper Currency Recognition; I. Introduction
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II. Small-Size Neuro-Recognition Technique Using the MasksIII. Mask Determination Using the Genetic Algorithm; IV. Development of the Neuro-Recognition Board Using the Digital Signal Processor; V. Unification of Three Core Techniques; VI. Conclusions; References; Chapter 5. Neural Network Classification Reliability: Problems and Applications; I. Introduction; II. Classification Paradigms; III. Neural Network Classifiers; IV. Classification Reliability; V. Evaluating Neural Network Classification Reliability; VI. Finding a Reject Rule; VII. Experimental Results; VIII. Summary; References
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Chapter 6. Parallel Analog Image Processing: Solving Regularization Problems with Architecture Inspired by the Vertebrate Retinal CircuitI. Introduction; II. Physiological Background; III. Regularization Vision Chips; IV. Spatio-Temporal Stability of Vision Chips; References; Chapter 7. Algorithmic Techniques and Their Applications; I. Introduction; II. Quasi-Newton Methods for Neural Network Training; III. Selecting the Number of Output Units; IV. Determining the Number of Hidden Units; V. Selecting the Number of Input Units; VI. Determining the Network Connections by Pruning
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VII. Applications of Neural Networks to Data MiningVIII. Summary; References; Chapter 8. Learning Algorithms and Applications of Principal Component Analysis; I. Introduction; II. Adaptive Learning Algorithm; III. Simulation Results; IV. Applications; V. Conclusion; VI. Appendix; References; Chapter 9. Learning Evaluation and Pruning Techniques; I. Introduction; II. Complexity Regularization; III. Sensitivity Calculation; IV. Optimization through Constraint Satisfaction; V. Local and Distributed Bottlenecks; VI. Interactive Pruning; VII. Other Pruning Methods; VIII. Concluding Remarks
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References
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English
Additional Edition:
ISBN 0-12-443865-2
Language:
English
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