A computational framework for segmentation and grouping /

This book represents a summary of the research we have been conducting since the early 1990s, and describes a conceptual framework which addresses some current shortcomings, and proposes a unified approach for a broad class of problems. While the framework is defined, our research continues, and som...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Medioni, G�erard
Otros Autores: Lee, Mi-Suen, Tang, Chi-Keung
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam ; New York : Elsevier, 2000.
Temas:
Computer vision.
Calculus of tensors.
Vision par ordinateur.
Calcul tensoriel.
COMPUTERS > Computer Vision & Pattern Recognition.
Calculus of tensors
Computer vision
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Table of Contents
  • List of Figures
  • Preface
  • Acknowledgements
  • Chapter 1. Introduction
  • 1.1 Motivation and Goals
  • 1.2 Our Approach
  • 1.3 Overview of the Proposed Method
  • 1.4 Contribution of this book
  • 1.5 Notations
  • Chapter 2. Previous Work
  • 2.1 Regularization
  • 2.2 Consistent Labeling
  • 2.3 Clustering and Robust Methods
  • 2.4 Artificial Neural Network Approach
  • 2.5 Novelty of Our Approach
  • Chapter 3. The Salient Feature Inference Engine
  • 3.1 Overview of the Salient Inference Engine
  • 3.2 Representation
  • 3.3 Communication through Tensor Voting
  • 3.4 Derivation and Properties of the Fundamental Voting Field
  • 3.5 Implementation of Tensor Voting
  • 3.6 Feature Extraction
  • 3.7 Complexity
  • 3.8 Summary
  • Chapter 4. Feature Extraction
  • 4.1 Extremal Curves in 2-D
  • 4.2 Extremal Surfaces in 3-D
  • 4.3 Extremal Curves in 3-D
  • 4.4 Complexity
  • 4.5 Summary
  • Chapter 5. Feature Inference in 2-D
  • 5.1 Related work
  • 5.2 Inference of junctions and curves from oriented data
  • 5.3 Inference of junctions and curves from non-oriented data
  • 5.4 Interesting properties
  • 5.5 End-point grouping
  • 5.6 Detection of curve end-points and region boundaries
  • 5.7 Integrated feature extraction in 2-D
  • 5.8 Applications
  • 5.9 Summary
  • Chapter 6. Feature Inference in 3-D
  • 6.1 Related Work
  • 6.2 Feature inference from oriented and non-oriented data
  • 6.3 Feature inference from oriented data
  • 6.4 Feature inference from non-oriented data
  • 6.5 Examples
  • 6.6 Integrated feature inference in 3-D
  • 6.7 Experiments
  • 6.8 Applications
  • 6.9 Summary
  • Chapter 7. Application to Early Vision Problems
  • 7.1 Shape from Shading
  • 7.2 Shape from Stereo
  • 7.3 Accurate Motion Flow Estimation with Discontinuities
  • Chapter 8. Conclusion
  • 8.1 Summary
  • 8.2 Future Research
  • Appendix A: Tensor analysis
  • Appendix B: Details of the Marching Algorithms
  • Appendix C: Software Systems
  • References
  • Author Index
  • Index
  • Color Plate Section
  • Last Page.

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