Natural Image Statistics

A Probabilistic Approach to Early Computational Vision

  • Authors
  • Aapo Hyvärinen
  • Jarmo Hurri
  • Patrik O. Hoyer
Part of the Computational Imaging and Vision book series (CIVI, volume 39)

Table of contents

  1. Front Matter
    Pages i-xix
  2. Introduction

    1. Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer
      Pages 1-21
  3. Background

    1. Front Matter
      Pages 23-23
    2. Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer
      Pages 25-49
    3. Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer
      Pages 51-66
    4. Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer
      Pages 67-90
  4. Statistics of Linear Features

    1. Front Matter
      Pages 91-91
    2. Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer
      Pages 93-130
    3. Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer
      Pages 131-150
    4. Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer
      Pages 151-175
    5. Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer
      Pages 177-196
  5. Nonlinear Features and Dependency of Linear Features

    1. Front Matter
      Pages 197-197
    2. Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer
      Pages 199-211
    3. Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer
      Pages 213-237
    4. Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer
      Pages 239-261
    5. Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer
      Pages 263-276
    6. Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer
      Pages 277-293
    7. Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer
      Pages 295-306
  6. Time, Color, and Stereo

    1. Front Matter
      Pages 307-307
    2. Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer
      Pages 309-323

About this book

Introduction

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 visual system to perform efficient probabilistic inference. The same framework is also very useful in engineering applications such as image processing and computer vision.

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.

Keywords

Computational Neuroscience Excel Information Machine Learning Neuroscience Stereo Vision Visual Neuroscience coding correlation discrete Fourier transform image processing signal processing

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-84882-491-1
  • Copyright Information Springer London 2009
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-84882-490-4
  • Online ISBN 978-1-84882-491-1
  • Series Print ISSN 1381-6446