Pattern Recognition in Practice IV : Multiple Paradigms, Comparative Studies and Hybrid Systems

By: Gelsema, E. SContributor(s): Kanal, L. NMaterial type: TextTextSeries: eBooks on DemandMachine Intelligence and Pattern Recognition: Publisher: Amsterdam : Elsevier Science, 2015Description: 1 online resource (593 p.)ISBN: 9781483297842Subject(s): Artificial intelligence -- Congresses | Image processing -- Congresses | Pattern perception -- Congresses | Signal processing -- CongressesGenre/Form: Electronic books.Additional physical formats: Print version:: Pattern Recognition in Practice IV: Multiple Paradigms, Comparative Studies and Hybrid SystemsDDC classification: 006.4 LOC classification: Q327 -- .P379 1994Online resources: Click here to view this ebook.
Contents:
Front Cover -- Pattem Recognition in Practice IV: Multiple Paradigms, Comparative Studies and Hybrid Systems -- Copyright Page -- Table of Contents -- PREFACE -- ACKNOWLEDGEMENTS -- PART I: PATTERN RECOGNITION -- Chapter 1. Patterns in the role of knowledge representation -- 1. INTRODUCEN -- 2. PATTERN REPRESENTATION -- 3. PATTERN RELATIONS -- 4. METMIC -- 5. HUMAN INTERACTION -- 6. PROJECTS -- 7. CONCLUSION -- APPENDIX -- REFERENCES -- Chapter 2. Application of evidence theory to k-NN pattern classification -- 1. D-S THEORY -- 2. THE METHOD -- 3. SIMULATION RESULTS -- 4. CONCLUSION
REFERENCES -- Chapter 3. Decision trees and domain knowledge in pattern recognition -- 1. INTRODUCTION -- 2. OVERVIEW OF DECISION TREE METHODOLOGIES -- 3. A FRAMEWORK FOR DECISION TREE CONSTRUCTION -- 4. EXPERIMENTAL EVALUATION -- 5. CONCLUSIONS -- REFERENCES -- Chapter 4. Object recognition using hidden Markov models -- 1. INTRODUCTION -- 2. STATISTICAL OBJECT RECOGNITION -- 3. HIDDEN MARKOV MODELS -- 4. OBJECT ORIENTED IMPLEMENTATION OF HMMS -- 5. AFFINE INVARIANT FEATURES -- 6. EXPERIMENTAL RESULTS -- 7. SUMMARY AND CONCLUSIONS -- ACKNOWLEDGEMENT -- REFERENCES
Chapter 5. Inference of syntax for point sets -- 1. INTRODUCTION -- 2. CHUNKING -- 3. LOW ORDER MOMENT DESCRIPTORS -- 4. INFERENCE -- 5. GENERALISATION -- 6. INVARIANCE -- 7. NOISE -- 8. BINDING AND OCCLUSION -- 9. NEURAL MODELS -- 10. SUMMARY AND CONCLUSIONS -- REFERENCES -- Chapter 6. Recognising cubes in images -- 1. INTRODUCTION -- 2. FINDING LINE SEGMENTS -- 3. FINDING SQUARES -- 4. FINDING CUBES -- 5. NOISE -- 6. INVARIANCE AND MANIFOLDS -- 7. OCCLUSION -- 8. CONCLUSION -- REFERENCES -- Chapter 7. Syntactic pattern classification of moving objects in a domestic environment
1. INTRODUCTION -- 2. METHOD -- 3. RESULTS -- 4. ROBUSTNESS -- 5. CONCLUSION AND FURTHER WORK -- REFERENCES -- Chapter 8. Initializing the EM algorithm for use in Gaussian mixture modelling -- 1. INTRODUCTION -- 2. THE EM ALGORITHM -- 3. CONVERGENCE AND INITIAL CONDITIONS -- 4. CLUSTERING TECHNIQUES -- 5. THE DOG RABBIT STRATEGY -- 6. RESULTS -- 7. CONCLUSION AND SUMMARY -- REFERENCES -- Chapter 9. Predicting REM in sleep EEG using a structural approach -- 1. INTRODUCTION -- 2. MODELING FRAMEWORK -- 3. METHODOLOGY -- 4. EXAMPLE -- 5. CONCLUSIONS -- REFERENCES -- Discussions Part I: Paper Vamos
PART II: SIGNAL- AND IMAGE PROCESSING -- Chapter 10. On the problem of restoring original structure of signals (images) corrupted by noise -- 1. INTRODUCTION -- 2. PIECE-WISE-LINEAR REGRESSION -- 3. MODEL SELECTION PROBLEM -- 4. APPLICATION TO IMAGE ANALYSIS -- 5. ROBUST REGRESSION AND HOUGH TRANSFORM -- 6. SUMMARY -- REFERENCES -- Chapter 11. Reflectance ratios: An extension of Land's retinex theory -- 1. INTRODUCTION -- 2. REFLECTANCE RATIOS -- 3. RECOGNITION USING REFLECTANCE RATIOS -- 4. EXPERIMENTS -- 5. DISCUSSION -- REFERENCES -- Chapter 12. A segmentation algorithm based on AI techniques
1. INTRODUCTION
Summary: The era of detailed comparisons of the merits of techniques of pattern recognition and artificial intelligence and of the integration of such techniques into flexible and powerful systems has begun. So confirm the editors of this fourth volume of Pattern Recognition in Practice, in their preface to the book. The 42 quality papers are sourced from a broad range of international specialists involved in developing pattern recognition methodologies and those using pattern recognition techniques in their professional work. The publication is divided into six sections: Pattern Recognition, Signal an
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Front Cover -- Pattem Recognition in Practice IV: Multiple Paradigms, Comparative Studies and Hybrid Systems -- Copyright Page -- Table of Contents -- PREFACE -- ACKNOWLEDGEMENTS -- PART I: PATTERN RECOGNITION -- Chapter 1. Patterns in the role of knowledge representation -- 1. INTRODUCEN -- 2. PATTERN REPRESENTATION -- 3. PATTERN RELATIONS -- 4. METMIC -- 5. HUMAN INTERACTION -- 6. PROJECTS -- 7. CONCLUSION -- APPENDIX -- REFERENCES -- Chapter 2. Application of evidence theory to k-NN pattern classification -- 1. D-S THEORY -- 2. THE METHOD -- 3. SIMULATION RESULTS -- 4. CONCLUSION

REFERENCES -- Chapter 3. Decision trees and domain knowledge in pattern recognition -- 1. INTRODUCTION -- 2. OVERVIEW OF DECISION TREE METHODOLOGIES -- 3. A FRAMEWORK FOR DECISION TREE CONSTRUCTION -- 4. EXPERIMENTAL EVALUATION -- 5. CONCLUSIONS -- REFERENCES -- Chapter 4. Object recognition using hidden Markov models -- 1. INTRODUCTION -- 2. STATISTICAL OBJECT RECOGNITION -- 3. HIDDEN MARKOV MODELS -- 4. OBJECT ORIENTED IMPLEMENTATION OF HMMS -- 5. AFFINE INVARIANT FEATURES -- 6. EXPERIMENTAL RESULTS -- 7. SUMMARY AND CONCLUSIONS -- ACKNOWLEDGEMENT -- REFERENCES

Chapter 5. Inference of syntax for point sets -- 1. INTRODUCTION -- 2. CHUNKING -- 3. LOW ORDER MOMENT DESCRIPTORS -- 4. INFERENCE -- 5. GENERALISATION -- 6. INVARIANCE -- 7. NOISE -- 8. BINDING AND OCCLUSION -- 9. NEURAL MODELS -- 10. SUMMARY AND CONCLUSIONS -- REFERENCES -- Chapter 6. Recognising cubes in images -- 1. INTRODUCTION -- 2. FINDING LINE SEGMENTS -- 3. FINDING SQUARES -- 4. FINDING CUBES -- 5. NOISE -- 6. INVARIANCE AND MANIFOLDS -- 7. OCCLUSION -- 8. CONCLUSION -- REFERENCES -- Chapter 7. Syntactic pattern classification of moving objects in a domestic environment

1. INTRODUCTION -- 2. METHOD -- 3. RESULTS -- 4. ROBUSTNESS -- 5. CONCLUSION AND FURTHER WORK -- REFERENCES -- Chapter 8. Initializing the EM algorithm for use in Gaussian mixture modelling -- 1. INTRODUCTION -- 2. THE EM ALGORITHM -- 3. CONVERGENCE AND INITIAL CONDITIONS -- 4. CLUSTERING TECHNIQUES -- 5. THE DOG RABBIT STRATEGY -- 6. RESULTS -- 7. CONCLUSION AND SUMMARY -- REFERENCES -- Chapter 9. Predicting REM in sleep EEG using a structural approach -- 1. INTRODUCTION -- 2. MODELING FRAMEWORK -- 3. METHODOLOGY -- 4. EXAMPLE -- 5. CONCLUSIONS -- REFERENCES -- Discussions Part I: Paper Vamos

PART II: SIGNAL- AND IMAGE PROCESSING -- Chapter 10. On the problem of restoring original structure of signals (images) corrupted by noise -- 1. INTRODUCTION -- 2. PIECE-WISE-LINEAR REGRESSION -- 3. MODEL SELECTION PROBLEM -- 4. APPLICATION TO IMAGE ANALYSIS -- 5. ROBUST REGRESSION AND HOUGH TRANSFORM -- 6. SUMMARY -- REFERENCES -- Chapter 11. Reflectance ratios: An extension of Land's retinex theory -- 1. INTRODUCTION -- 2. REFLECTANCE RATIOS -- 3. RECOGNITION USING REFLECTANCE RATIOS -- 4. EXPERIMENTS -- 5. DISCUSSION -- REFERENCES -- Chapter 12. A segmentation algorithm based on AI techniques

1. INTRODUCTION

The era of detailed comparisons of the merits of techniques of pattern recognition and artificial intelligence and of the integration of such techniques into flexible and powerful systems has begun. So confirm the editors of this fourth volume of Pattern Recognition in Practice, in their preface to the book. The 42 quality papers are sourced from a broad range of international specialists involved in developing pattern recognition methodologies and those using pattern recognition techniques in their professional work. The publication is divided into six sections: Pattern Recognition, Signal an

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