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Digital color imaging /
This collective work identifies the latest developments in the field of the automatic processing and analysis of digital color images. For researchers and students, it represents a critical state of the art on the scientific issues raised by the various steps constituting the chain of color image pr...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London : Hoboken :
ISTE ; John Wiley & Sons,
2012.
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| Colección: | Digital signal and image processing series.
ISTE. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright Page; Table of Contents; Foreword; Chapter 1. Color Representation and Processing in Polar Color Spaces; 1.1. Introduction; 1.1.1. Notations used in this chapter; 1.2. The HSI triplet; 1.2.1. Intuitive approach: basic concepts and state of the art; 1.2.2. Geometric approach: calculation of polar coordinates; 1.3. Processing of hue: a variable on the unit circle; 1.3.1. Can hue be represented as a scalar?; 1.3.2. Ordering based on distance from a reference hue; 1.3.3. Ordering with multiple references; 1.3.4. Determination of reference hues.
- 1.4. Color morphological filtering in the HSI space1.4.1. Chromatic and achromatic top-hat transforms; 1.4.2. Full ordering using lexicographical cascades; 1.5. Morphological color segmentation in the HSI space; 1.5.1. Color distances and segmentation by connective criteria; 1.5.2. Color gradients and watershed segmentation; 1.6. Conclusion; 1.7. Bibliography; Chapter 2. Adaptive Median Color Filtering; 2.1. Introduction; 2.2. Noise; 2.2.1. Sources of noise; 2.2.2. Noise modeling; 2.3. Nonlinear filtering; 2.3.1. Vector methods; 2.3.2. Median filter using bit mixing.
- 2.4. Median filter: methods derived from vector methods2.4.1. Vector filtering; 2.4.2. Switching vector and peer group filters; 2.4.3. Hybrid switching vector filter; 2.4.4. Fuzzy filters; 2.5. Adaptive filters; 2.5.1. Spatially adaptive filter: generic method; 2.5.2. Spatially adaptive median filter; 2.6. Performance comparison; 2.6.1. FSVF; 2.6.2. FRF; 2.6.3. PGF and FMPGF; 2.6.4. IPGSVF; 2.6.5. Vector filters and spatially adaptive median filter; 2.7. Conclusion; 2.8. Bibliography; Chapter 3. Anisotropic Diffusion PDEs for Regularization of Multichannel Images: Formalisms and Applications.
- 3.1. Introduction3.2. Preliminary concepts; 3.3. Local geometry in multi-channel images; 3.3.1. Which geometric characteristics?; 3.3.2. Geometry estimated using a scalar characteristic; 3.3.3. Di Zenzo multi-valued geometry; 3.4. PDEs for multi-channel image smoothing: overview; 3.4.1. Variational methods; 3.4.2. Divergence PDEs; 3.4.3. Oriented Laplacian PDEs; 3.4.4. Trace PDEs; 3.5. Regularization and curvature preservation; 3.5.1. Single smoothing direction; 3.5.2. Analogy with line integral convolutions; 3.5.3. Extension to multi-directional smoothing; 3.6. Numerical implementation.
- 3.7. Some applications3.8. Conclusion; 3.9. Bibliography; Chapter 4. Linear Prediction in Spaces with Separate Achromatic and Chromatic Information; 4.1. Introduction; 4.2. Complex vector 2D linear prediction; 4.3. Spectral analysis in the IHLS and L*a*b* color spaces; 4.3.1. Comparison of PSD estimation methods; 4.3.2. Study of inter-channel interference associated with color space changing transformations; 4.4. Application to segmentation of textured color images; 4.4.1. Prediction error distribution; 4.4.2. Label field estimation; 4.4.3. Experiments and results; 4.5. Conclusion.