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.
BACKGROUND Introduction Statistics as Priors Statistics as Image Descriptors Statistical Pipeline Natural Images Discussion 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 IMAGE STATISTICS First Order Statistics Histograms and Moments Moment Statistics and Average Distributions Material Properties Nonlinear Compression in Art Dark-Is-Deep Paradigm Summary Gradients, Edges, and Contrast Real-World Considerations Gradients Edges Linear Scale Space Contrast in Images Image Deblurring Super Resolution Inpainting Fourier Analysis Auto-Correlation 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 Graphs Probabilities and Markov Random Fields MAP-MRF Applications Complex Models and Patch-Based Regularities Statistical Analysis of MRFs BEYOND TWO DIMENSIONS Color Trichromacy and Metamerism Color as a 3D Space Opponent Processing Color Transfer Color Space Statistics Color Constancy and White Balancing Summary 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 Motion Applications That Use Statistical Motion Regularities Optical Flow Appendix: Basic Definitions Bibliography
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