Natural Image Stattistics A Probabilistic Approach to Early Computational vision 1st Edition by Aapo Hyvarinen, Jarmo Hurri, Patrick O Hoyer ISBN 1848824904 9781848824904

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ISBN 10: 1848824904 
ISBN 13: 9781848824904
Author: Aapo Hyvarinen, Jarmo Hurri, Patrick O Hoyer

Aims and Scope This book is both an introductory textbook and a research monograph on modeling the statistical structure of natural images. In very simple terms, “natural images” are photographs of the typical environment where we live. In this book, their statistical structure is described using a number of statistical models whose parameters are estimated from image samples. Our main motivation for exploring natural image statistics is computational m- eling of biological visual systems. A theoretical framework which is gaining more and more support considers the properties of the visual system to be re?ections of the statistical structure of natural images because of evolutionary adaptation processes. Another motivation for natural image statistics research is in computer science and engineering, where it helps in development of better image processing and computer vision methods. While research on natural image statistics has been growing rapidly since the mid-1990s, no attempt has been made to cover the ?eld in a single book, providing a uni?ed view of the different models and approaches. This book attempts to do just that. Furthermore, our aim is to provide an accessible introduction to the ?eld for students in related disciplines.

Natural Image Stattistics A Probabilistic Approach to Early Computational vision 1st Table of contents:

• Introduction
  1.1. Overview of Computational Vision
  1.2. The Role of Natural Image Statistics in Vision
  1.3. Probabilistic Modeling in Image Processing
  1.4. Goals and Scope of the Book
  1.5. Outline of the Chapters

• Foundations of Natural Image Statistics
  2.1. What Are Natural Images?
  2.2. Statistical Properties of Natural Images
  2.3. Image Representations: Pixels, Features, and Transformations
  2.4. The Role of Spatial and Temporal Structure
  2.5. Challenges in Modeling Image Statistics

• Probabilistic Models for Image Statistics
  3.1. Introduction to Probabilistic Models
  3.2. Gaussian Models and Their Limitations
  3.3. Higher-Order Statistics and Non-Gaussian Distributions
  3.4. Markov Random Fields in Image Modeling
  3.5. Bayesian Inference and the Image Processing Pipeline

• Linear and Nonlinear Image Transformations
  4.1. The Fourier Transform and Frequency Analysis
  4.2. Wavelets and Multiresolution Analysis
  4.3. Nonlinear Image Processing Techniques
  4.4. Applications of Linear and Nonlinear Transformations
  4.5. Statistical Approaches for Feature Extraction

• Statistical Properties of Image Features
  5.1. Feature Detection and Representation
  5.2. Statistical Models for Edges, Corners, and Textures
  5.3. Feature Co-occurrence and Dependency Modeling
  5.4. Dimensionality Reduction Techniques
  5.5. Probabilistic Approaches for Feature Matching and Correspondence

• Image Segmentation and Clustering
  6.1. Introduction to Image Segmentation
  6.2. Statistical Models for Image Segmentation
  6.3. Clustering Algorithms Based on Image Statistics
  6.4. Probabilistic Graphical Models in Segmentation
  6.5. Performance Metrics for Segmentation Algorithms

• Learning from Image Statistics
  7.1. Supervised vs. Unsupervised Learning
  7.2. Estimating Image Statistics from Data
  7.3. Learning the Probabilistic Structure of Images
  7.4. Statistical Models for Object Recognition
  7.5. Integrating Learning with Computational Vision Systems

• Applications of Natural Image Statistics
  8.1. Image Denoising and Restoration
  8.2. Image Compression Using Statistical Models
  8.3. Object Detection and Tracking with Probabilistic Models
  8.4. Texture Synthesis and Image Generation
  8.5. Medical Imaging and Computer Vision

• Advanced Topics in Image Statistics
  9.1. Higher-Dimensional Image Statistics (Color, Stereo, etc.)
  9.2. Non-Stationary and Dynamic Image Models
  9.3. The Role of Temporal Information in Image Processing
  9.4. Neural Networks and Deep Learning in Image Statistics
  9.5. Emerging Trends in Computational Vision and Image Modeling

• Challenges and Future Directions
  10.1. Unsolved Problems in Natural Image Statistics
  10.2. Multi-Scale and Multi-Modal Image Analysis
  10.3. Real-Time Image Processing and Vision Systems
  10.4. Towards Generalizable Models for Vision
  10.5. Conclusion and Open Research Areas

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