Image Processing and Pattern Recognition
Regular price
55.250 KD
inc. VAT
Couldn't load pickup availability
Table of contents
- Cover
- Contentsv
- Contributorsxiii
- Prefacexv
- Chapter 1. Pattern Recognition1
- I. Introduction1
- II. Pattern Recognition Problem3
- III. Neural Networks in Feature Extraction11
- IV. Classification Methods: Statistical and Neural20
- V. Neural Network Applications in Pattern Recognition38
- VI. Summary52
- References53
- Chapter 2. Comparison of Statistical and Neural Classifiers and Their Applications to Optical Charac61
- I. Introduction61
- II. Applications63
- III. Data Acquisition and Preprocessing64
- IV. Statistical Classifiers65
- V. Neural Classifiers74
- VI. Literature Survey79
- VII. Simulation Results81
- VIII. Conclusions85
- References86
- Chapter 3. Medical Imaging89
- I. Introduction89
- II. Review of Artificial Neural Network Applications in Medical Imaging95
- III. Segmentation of Arteriograms99
- IV. Back-Propagation Artificial Neural Network for Arteriogram Segmentation: A Supervised Approach101
- V. Self-Adaptive Artificial Neural Network for Arteriogram Segmentation: An Unsupervised Approach107
- VI. Conclusions124
- References129
- Chapter 4. Paper Currency Recognition133
- I. Introduction133
- II. Small-Size Neuro-Recognition Technique Using the Masks134
- III. Mask Determination Using the Genetic Algorithm143
- IV. Development of the Neuro-Recognition Board Using the Digital Signal Processor152
- V. Unification of Three Core Techniques156
- VI. Conclusions158
- References159
- Chapter 5. Neural Network Classification Reliability: Problems and Applications161
- I. Introduction161
- II. Classification Paradigms164
- III. Neural Network Classifiers167
- IV. Classification Reliability172
- V. Evaluating Neural Network Classification Reliability174
- VI. Finding a Reject Rule178
- VII. Experimental Results185
- VIII. Summary196
- References197
- Chapter 6. Parallel Analog Image Processing: Solving Regularization Problems with Architecture Inspi201
- I. Introduction201
- II. Physiological Background202
- III. Regularization Vision Chips221
- IV. Spatio-Temporal Stability of Vision Chips264
- References283
- Chapter 7. Algorithmic Techniques and Their Applications287
- I. Introduction287
- II. Quasi-Newton Methods for Neural Network Training289
- III. Selecting the Number of Output Units295
- IV. Determining the Number of Hidden Units296
- V. Selecting the Number of Input Units303
- VI. Determining the Network Connections by Pruning309
- VII. Applications of Neural Networks to Data Mining313
- VIII. Summary316
- References317
- Chapter 8. Learning Algorithms and Applications of Principal Component Analysis321
- I. Introduction321
- II. Adaptive Learning Algorithm324
- III. Simulation Results335
- IV. Applications343
- V. Conclusion349
- VI. Appendix350
- References351
- Chapter 9. Learning Evaluation and Pruning Techniques353
- I. Introduction353
- II. Complexity Regularization357
- III. Sensitivity Calculation362
- IV. Optimization through Constraint Satisfaction368
- V. Local and Distributed Bottlenecks372
- VI. Interactive Pruning374
- VII. Other Pruning Methods376
- VIII. Concluding Remarks378
- References378
- Index383
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 applications methodologies.
Key Features
* Coverage includes:
* Optical character recognition
* Speech classification
* Medical imaging
* Paper currency recognition
* Classification reliability techniques
* Sensor technology
Algorithms and architectures for achieving practical and effective systems are emphasized, with many examples illustrating the text. Practitioners, researchers, and students in computer science, electrical engineering, and radiology, as wellk as those working at financial institutions, will find this volume a unique and comprehensive reference source for this diverse applications area.
Key Features
* Coverage includes:
* Optical character recognition
* Speech classification
* Medical imaging
* Paper currency recognition
* Classification reliability techniques
* Sensor technology
Algorithms and architectures for achieving practical and effective systems are emphasized, with many examples illustrating the text. Practitioners, researchers, and students in computer science, electrical engineering, and radiology, as wellk as those working at financial institutions, will find this volume a unique and comprehensive reference source for this diverse applications area.