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Natural Image Statistics

A Probabilistic Approach to Early Computational Vision.

By Aapo Hyvärinen , Jarmo Hurri , Patrick O. Hoyer

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This is the first comprehensive introduction to the multidisciplinary field of natural image statistics. It offers a clear overview of all the basic theory plus the most recent developments and research, and it includes exercises and computer assignments.

Full Description

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  • ISBN13: 978-1-8488-2490-4
  • 468 Pages
  • User Level: Science
  • Publication Date: April 21, 2009
  • Available eBook Formats: PDF
Full Description
This book is the first comprehensive introduction to the multidisciplinary field of natural image statistics and its intention is to present a general theory of early vision and image processing in a manner that can be approached by readers from a variety of scientific backgrounds. A wealth of relevant background material is presented in the first section as an introduction to the subject. Following this are five unique sections, carefully selected so as to give a clear overview of all the basic theory, as well as the most recent developments and research. This structure, together with the included exercises and computer assignments, also make it an excellent textbook. Natural Image Statistics is a timely and valuable resource for advanced students and researchers in any discipline related to vision, such as neuroscience, computer science, psychology, electrical engineering, cognitive science or statistics.
Table of Contents

Table of Contents

  1. 1. Introduction.
  2. Part I Background.
  3. 2. Linear Filters and Frequency Analysis.
  4. 3. Outline of the Visual System.
  5. 4. Multivariate Probability and Statistics.
  6. Part II Statistics of Linear Features.
  7. 5. Principal Components and Whitening.
  8. 6. Sparse Coding and Simple Cells.
  9. 7. Independent Component Analysis.
  10. 8. Information
  11. Theoretic Interpretations.
  12. Part III Nonlinear Features and Dependency of Linear Features.
  13. 9. Energy Correlation of Linear Features and Normalisation.
  14. 10. Energy Detectors and Complex Cells.
  15. 11. Energy Correlations and Topographic Organisation.
  16. 12. Dependencies of Energy Detectors; Beyond V1.
  17. 13. Overcomplete and Non
  18. Negative Models.
  19. 14. Lateral Interactions and Feedback.
  20. Part IV Time, Colour and Stereo.
  21. 15. Colour and Stereo Images.
  22. 16. Temporal Sequences of Natural Images.
  23. Part V Conclusion.
  24. 17. Conclusion and Future Prospects.
  25. Part VI Appendix: Supplementary Mathematical Tools.
  26. 18. Optimisation Theory and Algorithms.
  27. 19. Crash Course on Linear Algebra.
  28. 20. The Discrete Fourier Transform.
  29. 21. Estimation of Non
  30. Normalised Statistical Models.
  31. Index.
  32. References.

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