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Mathematics and computation in imaging science and information processing /
The explosion of data arising from rapid advances in communication, sensing and computational power has concentrated research effort on more advanced techniques for the representation, processing, analysis and interpretation of data sets. In view of these exciting developments, the program "Mat...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New Jersey :
World Scientific,
©2007.
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| Colección: | Lecture notes series (National University of Singapore. Institute for Mathematical Sciences) ;
v. 11. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Foreword; Preface; Subdivision on Arbitrary Meshes: Algorithms and Theory Denis Zorin; 1. Introduction; 1.1. Subdivision in computer graphics and geometric modeling; 2. Basics; 2.1. Subdivision curves; 2.2. Subdivision surfaces; 3. Overview of Subdivision Schemes; 3.1. Classification of subdivision schemes; 3.2. Loop scheme; 3.3. Catmull-Clark scheme; 4. Modified Buttery Scheme; 4.1. Doo-Sabin scheme; 4.2. Midedge scheme and other non-integer arity schemes; 4.3. Comparison; 5. Smoothness of Subdivision Surfaces; 5.1. Cr-continuity and tangent plane continuity.
- 5.2. Universal surfaces5.3. Sufficient smoothness criteria; 6. Approximation Properties of Subdivision Surfaces; 6.1. Functional spaces on surfaces; 6.2. Manifold structure defined by subdivision; 7. Conclusions; References; High Order Numerical Methods for Time Dependent Hamilton-Jacobi Equations Chi-Wang Shu; 1. Introduction and Properties of Hamilton-Jacobi Equations; 2. First Order Monotone Schemes; 2.1. Monotone schemes on structured rectangular meshes; 2.2. Monotone schemes on unstructured meshes; 3. High Order ENO and WENO Schemes on Structured Rectangular Meshes.
- 3.1. High order ENO schemes3.2. High order WENO schemes; 4. High Order WENO Schemes on Unstructured Meshes; 5. High Order Discontinuous Galerkin Schemes on Unstructured Meshes; 6. High Order Strong Stability Preserving Runge-Kutta Time Discretizations; 7. A Few Numerical Examples; 8. Concluding Remarks; References; Theory and Computation of Variational Image Deblurring Tony F. Chan and Jianhong Shen; 1. Mathematical Models of Blurs; 1.1. Linear blurs; 1.2. The DC-condition; 1.3. Nonlinear blurs; 2. Illposedness of Deblurring; 3. Tikhonov and Bayesian Regularization.
- 4. Optimal Wiener Filtering for Non-Blind Deblurring4.1. 2-D stochastic spatial signals; 4.2. Stochastic signals as random generalized functions; 4.3. Filtering-based deblurring; 4.4. Optimal Wiener ltering; 4.5. Connection to the Bayesian/Tikhonov method; 5. Deblurring Blurred BV Images; 5.1. TV deblurring by Rudin, Osher, and Fatemi; 5.2. Dealing with bounded image domains; 5.3. Existence and uniqueness; 5.4. Computation and examples; 6. Parametric Blind Deblurring; 6.1. Parametric modeling; 6.2. The AM algorithm; 7. Non-Parametric Blind Deblurring: Double-BV Model.
- 7.1. General formulation of blind deblurring7.2. Double-BV blind deblurring model of Chan and Wong; 7.3. On the uniqueness: Hidden symmetries; 7.4. The existence theorem; 8. Deblurring Besov Images via Iterated Shrinkage; 8.1. Wavelets and Besov images; 8.2. Besov image deblurring via iterated shrinkage; 8.3. Understanding the iterated-shrinkage algorithm; 8.3.1. As semi-implicit time marching; 8.3.2. Via augmentation and auxiliary variables; 9. Further Reading; Acknowledgements; References; Data Hiding
- Theory and Algorithms Pierre Moulin and Ralf Koetter; 1. Introduction.