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.
Why are We Writing This Book?
Fundamentals covered in the book include convolution, Fourier transform, filters, geometric transformations, epipolar geometry, 3D reconstruction, color and the image synthesis pipeline. The book is organized in four parts.
Aims and Scope This book is both an introductory textbook and a research monograph on modeling the statistical structure of natural images.
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In this volume, Violet Leavers, an established author and researcher in the field, examines the Hough Transform, a technique which is particularly relevant to industrial applications.
Core Concepts in Computer Vision, Graphics, and Image Processing Aditi Majumder, M. Gopi. Figure 1.13. This figure illustrates random noise in 1D audio data (left), 2D image data (middle), and 3D surface data (right).
image. Data Sets ... . . . nsing Techniques The visual interpretation of high-dimensional data sets is an emerging subfield in scientific visualization and has become increasingly important. For this task, it is necessary to analyze the ...
The two volume set LNCS 5358 and LNCS 5359 constitutes the refereed proceedings of the 4th International Symposium on Visual Computing, ISVC 2008, held in Las Vegas, NV, USA, in December 2008.
In: Color Imaging Conference (2004) Gijsenij, A., Gevers, T.: Color constancy using natural image statistics. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007) Hsu, E., Mertens, T., Paris, ...
The goal of this book is to introduce the reader to the recent advances from the field of uncertainty quantification and error propagation for computer vision, image processing, and image analysis that are based on partial differential ...