בחרו את שפת העמוד:
שפה נוכחית
כניסה עם גוגל
כניסה עם פייסבוק
Image processing and pattern recognition [electronic resource]
להגדלת הטקסט להקטנת הטקסט- ספר
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
| כותר |
Image processing and pattern recognition [electronic resource] / edited by Cornelius T. Leondes. |
|---|---|
| מוציא לאור |
San Diego : Academic Press |
| שנה |
c1998 |
| הערות |
Description based upon print version of record. Includes bibliographical references and index. English |
| הערת תוכן ותקציר |
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 II. Applications III. Data Acquisition and Preprocessing IV. Statistical ClassifiersV. Neural Classifiers VI. Literature Survey VII. Simulation Results VIII. Conclusions Chapter 3. Medical Imaging 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 Chapter 4. Paper Currency Recognition 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 Chapter 5. Neural Network Classification Reliability: Problems and Applications 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 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 Chapter 7. Algorithmic Techniques and Their Applications 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 VII. Applications of Neural Networks to Data MiningVIII. Summary Chapter 8. Learning Algorithms and Applications of Principal Component Analysis II. Adaptive Learning Algorithm III. Simulation Results IV. Applications V. Conclusion VI. Appendix Chapter 9. Learning Evaluation and Pruning Techniques 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 References |
| סדרה |
Neural network systems, techniques, and applications v. 5 |
| היקף החומר |
1 online resource (407 p.) |
| שפה |
אנגלית |
| מספר מערכת |
997010710277505171 |
תצוגת MARC
יודעים עוד על הפריט? זיהיתם טעות?
