372 Pages 166 Color Illustrations
    by A K Peters/CRC Press

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    To achieve the complex task of interpreting what we see, our brains rely on statistical regularities and patterns in visual data. Knowledge of these regularities can also be considerably useful in visual computing disciplines, such as computer vision, computer graphics, and image processing. The field of natural image statistics studies the regularities to exploit their potential and better understand human vision. With numerous color figures throughout, Image Statistics in Visual Computing covers all aspects of natural image statistics, from data collection to analysis to applications in computer graphics, computational photography, image processing, and art.

    The authors keep the material accessible, providing mathematical definitions where appropriate to help readers understand the transforms that highlight statistical regularities present in images. The book also describes patterns that arise once the images are transformed and gives examples of applications that have successfully used statistical regularities. Numerous references enable readers to easily look up more information about a specific concept or application. A supporting website also offers additional information, including descriptions of various image databases suitable for statistics.

    Collecting state-of-the-art, interdisciplinary knowledge in one source, this book explores the relation of natural image statistics to human vision and shows how natural image statistics can be applied to visual computing. It encourages readers in both academic and industrial settings to develop novel insights and applications in all disciplines that relate to visual computing.

    Statistics as Priors
    Statistics as Image Descriptors
    Statistical Pipeline
    Natural Images

    The Human Visual System
    Radiometric and Photometric Terms
    Human Vision
    The Eyes
    The Lateral Geniculate Nucleus and Cortical Processing
    Implications of Human Visual Processing

    Image Collection and Calibration
    Image Capture
    Post-Processing and Calibration
    Image Databases

    First Order Statistics
    Histograms and Moments
    Moment Statistics and Average Distributions
    Material Properties
    Nonlinear Compression in Art
    Dark-Is-Deep Paradigm

    Gradients, Edges, and Contrast
    Real-World Considerations
    Linear Scale Space
    Contrast in Images
    Image Deblurring
    Super Resolution

    Fourier Analysis
    The Fourier Transform
    The Wiener-Khintchine Theorem
    Power Spectra
    Phase Spectra
    Human Perception
    Fractal Forgeries
    Image Processing and Categorization
    Texture Descriptors
    Terrain Synthesis
    Art Statistics

    Dimensionality Reduction
    Principal Component Analysis
    Independent Components Analysis
    ICA on Natural Images
    Gaussian Mixture Models

    Wavelet Analysis
    Wavelet Transform
    Multiresolution Analysis
    Signal Processing
    Other Bases
    2D Wavelets
    Contourlets, Curvelets, and Ridgelets
    Coefficient Histograms
    Scale Invariance
    Correlations between Coefficients
    Complex Wavelets
    Correlations between Scales
    Application: Image Denoising
    Application: Progressive Reconstruction
    Application: Texture Synthesis

    Markov Random Fields
    Image Interpretation
    Probabilities and Markov Random Fields
    Complex Models and Patch-Based Regularities
    Statistical Analysis of MRFs


    Trichromacy and Metamerism
    Color as a 3D Space
    Opponent Processing
    Color Transfer
    Color Space Statistics
    Color Constancy and White Balancing

    Depth Statistics
    The "Dead Leaves" Model
    Perception of Scene Geometry
    Correlations between 2D and Range Statistics
    Depth Reconstruction

    Time and Motion
    The Statistics of Time
    Applications That Use Statistical Motion Regularities
    Optical Flow

    Appendix: Basic Definitions



    Tania Pouli, Erik Reinhard, Douglas W. Cunningham

    "This book is a survey of natural image statistics used in these days. It is presented in an accessible fashion full of color images. It contains more than 800 reference entries. So, it is a good starting point for all those who want to easily familiarize with the theory of the presented field. This book is good for computer scientists who want to start their research in digital imaging and for engineers who want to apply the described methods in practice."
    —Agnieszka Lisowska (Sosnowiec), in Zentralblatt MATH 1295