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Handbook of texture analysis /
Texture analysis is one of the fundamental aspects of human vision by which we discriminate between surfaces and objects. In a similar manner, computer vision can take advantage of the cues provided by surface texture to distinguish and recognize objects. In computer vision, texture analysis may be...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London : Singapore ; Hackensack, NJ :
Imperial College Press ; Distributed by World Scientific,
©2008.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Preface; Contents; Chapter 1 Introduction to Texture Analysis E.R. Davies; 1.1. Introduction
- The Idea of a Texture; 1.2. A Simple Texture Analysis Technique and Its Limitations; 1.3. Autocorrelation and Fourier Methods; 1.4. Grey-Level Co-Occurrence Matrices; 1.5. The Texture Energy Approach; 1.6. The Eigenfilter Approach; 1.7. Appraisal of the Texture Energy and Eigenfilter Approaches; 1.8. Problems with Texture Segmentation; 1.9. An X-Ray Inspection Application; 1.9.1. Further details of the algorithm; 1.10. Other Approaches to Texture Analysis; 1.10.1. Fractal-based measures of texture.
- 1.10.2. Markov random field models of texture1.10.3. Structural approaches to texture analysis; 1.10.4. 3D shape from texture; 1.10.5. More recent developments; 1.11. Concluding Remarks; Acknowledgements; References; Chapter 2 Texture Modelling and Synthesis R. Paget; 2.1. Introduction; 2.1.1. Texture perception; 2.2. Texture Analysis; 2.3. Texture Synthesis Modelling; 2.4. Milestones in Texture Synthesis; 2.4.1. Popat and Picard, '93: Novel cluster-based probability model for texture synthesis, classi cation, and compression. 43.
- 2.4.2. Heeger and Bergen, '95: Pyramid based texture analysis/synthesis402.4.3. De Bonet, '97: Multiresolution sampling procedure for analysis and synthesis of texture images39; 2.4.4. Zhu, Wu, and Mumford, '97, '98: FRAME: Filters, random elds and maximum entropy towards a uni ed theory for texture modelling46,47; 2.4.5. Simoncelli and Portilla, '98: Texture characterisation via joint statistics of wavelet coe cient magnitudes48; 2.4.6. Paget and Longsta, '98: Texture synthesis via a nonparametric Markov random eld model20.
- 2.4.7. Efros and Leung, '99: Texture synthesis by non-parametric sampling492.4.8. Wei and Levoy, '00: Fast texture synthesis using tree-structured vector quantisation50; 2.4.9. Zhu, Liu and Wu, '00: Julesz ensemble51; 2.4.10. Xu, Guo and Shum, '00: Chaos mosaic: fast and memory e cient texture synthesis53 and Y.Q. Xu, S.C. Zhu, B.N. Guo, and H.Y. Shum, '01 \Asymptotically Admissible Texture Synthesis""54; 2.4.11. Liang et al., '01: Real-time texture synthesis by patch-based sampling55; 2.4.12. Ashikhmin, '01: Synthesising natural textures56.
- 2.4.13. Hertzmann et al., '01 Image analogies: A general texture transfer framework572.4.14. Efros and Freeman, '01: Image quilting: stitch together patches of input image, texture transfer58; 2.4.15. Zelinka and Garland, '02: Towards Real-Time Texture Synthesis with the Jump Map59; 2.4.16. Tong et al., '02: Synthesis of bidirectional texture functions on arbitrary surfaces61; 2.4.17. Nealen and Alexa, '03: Hybrid texture synthesis62; 2.4.18. Kwatra et al., '03: Graphcut textures: Image and video synthesis using graph cuts64; 2.5. Further Developments; 2.6. Summary; References.