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Natural Image Statistics A Probabilistic Approach to Early Computational Vision. /
One of the most successful frameworks in computational neuroscience is modelling visual processing using the statistical structure of natural images. In this framework, the visual system of the brain constructs a model of the statistical regularities of the incoming visual data. This enables the vis...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Springer London : Imprint: Springer,
2009.
|
| Edición: | 1st ed. 2009. |
| Colección: | Computational Imaging and Vision ;
39 |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- Background
- Linear Filters and Frequency Analysis
- Outline of the Visual System
- Multivariate Probability and Statistics
- Statistics of Linear Features
- Principal Components and Whitening
- Sparse Coding and Simple Cells
- Independent Component Analysis
- Information-Theoretic Interpretations
- Nonlinear Features and Dependency of Linear Features
- Energy Correlation of Linear Features and Normalization
- Energy Detectors and Complex Cells
- Energy Correlations and Topographic Organization
- Dependencies of Energy Detectors: Beyond V1
- Overcomplete and Non-negative Models
- Lateral Interactions and Feedback
- Time, Color, and Stereo
- Color and Stereo Images
- Temporal Sequences of Natural Images
- Conclusion
- Conclusion and Future Prospects
- Appendix: Supplementary Mathematical Tools
- Optimization Theory and Algorithms
- Crash Course on Linear Algebra
- The Discrete Fourier Transform
- Estimation of Non-normalized Statistical Models.