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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Hyvärinen, Aapo (Συγγραφέας), Hurri, Jarmo (Συγγραφέας), Hoyer, Patrik O. (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: London : Springer London, 2009.
Σειρά:Computational Imaging and Vision, 39
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 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.