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Author: Edward R. Dougherty Publisher: SPIE Press ISBN: 9780819415608 Category : Technology & Engineering Languages : en Pages : 200
Book Description
From a strict semantic point of view, nonlinear image processing encompasses all image processing that is not based on linear operators; however, from a practical, evolutionary point of view, the name itself is usually associated with the study of nonlinear filters, mainly the deterministic and nondeterministic analysis and design of logic-based operators. This Tutorial Text volume explores logic-based operators with emphasis on representation, design, and statistical optimization of nonlinear filters.
Author: Edward R. Dougherty Publisher: SPIE Press ISBN: 9780819415608 Category : Technology & Engineering Languages : en Pages : 200
Book Description
From a strict semantic point of view, nonlinear image processing encompasses all image processing that is not based on linear operators; however, from a practical, evolutionary point of view, the name itself is usually associated with the study of nonlinear filters, mainly the deterministic and nondeterministic analysis and design of logic-based operators. This Tutorial Text volume explores logic-based operators with emphasis on representation, design, and statistical optimization of nonlinear filters.
Author: Sanjit Mitra Publisher: Academic Press ISBN: 9780125004510 Category : Computers Languages : en Pages : 480
Book Description
This state-of-the-art book deals with the most important aspects of non-linear imaging challenges. The need for engineering and mathematical methods is essential for defining non-linear effects involved in such areas as computer vision, optical imaging, computer pattern recognition, and industrial automation challenges.
Author: Edward R. Dougherty Publisher: SPIE Press ISBN: 9780780353855 Category : Computers Languages : en Pages : 480
Book Description
"This text covers key mathematical principles and algorithms for nonlinear filters used in image processing. Readers will gain an in-depth understanding of the underlying mathematical and filter design methodologies needed to construct and use nonlinear filters in a variety of applications. The 11 chapters explore topics of contemporary interest as well as fundamentals drawn from nonlinear filtering's historical roots in mathematical morphology and digital signal processing. This book examines various filter options and the types of applications for which they are best suited. The presentation is rigorous, yet accessible to engineers with a solid background in mathematics."
Author: Edward R. Dougherty Publisher: Springer Science & Business Media ISBN: 1461531489 Category : Computers Languages : en Pages : 173
Book Description
Mathematical Nonlinear Image Processing deals with a fast growing research area. The development of the subject springs from two factors: (1) the great expansion of nonlinear methods applied to problems in imaging and vision, and (2) the degree to which nonlinear approaches are both using and fostering new developments in diverse areas of mathematics. Mathematical Nonlinear Image Processing will be of interest to people working in the areas of applied mathematics as well as researchers in computer vision. Mathematical Nonlinear Image Processing is an edited volume of original research. It has also been published as a special issue of the Journal of Mathematical Imaging and Vision. (Volume 2, Issue 2/3).
Author: Guy Gilboa Publisher: Springer ISBN: 3319758470 Category : Computers Languages : en Pages : 186
Book Description
This unique text/reference presents a fresh look at nonlinear processing through nonlinear eigenvalue analysis, highlighting how one-homogeneous convex functionals can induce nonlinear operators that can be analyzed within an eigenvalue framework. The text opens with an introduction to the mathematical background, together with a summary of classical variational algorithms for vision. This is followed by a focus on the foundations and applications of the new multi-scale representation based on non-linear eigenproblems. The book then concludes with a discussion of new numerical techniques for finding nonlinear eigenfunctions, and promising research directions beyond the convex case. Topics and features: introduces the classical Fourier transform and its associated operator and energy, and asks how these concepts can be generalized in the nonlinear case; reviews the basic mathematical notion, briefly outlining the use of variational and flow-based methods to solve image-processing and computer vision algorithms; describes the properties of the total variation (TV) functional, and how the concept of nonlinear eigenfunctions relate to convex functionals; provides a spectral framework for one-homogeneous functionals, and applies this framework for denoising, texture processing and image fusion; proposes novel ways to solve the nonlinear eigenvalue problem using special flows that converge to eigenfunctions; examines graph-based and nonlocal methods, for which a TV eigenvalue analysis gives rise to strong segmentation, clustering and classification algorithms; presents an approach to generalizing the nonlinear spectral concept beyond the convex case, based on pixel decay analysis; discusses relations to other branches of image processing, such as wavelets and dictionary based methods. This original work offers fascinating new insights into established signal processing techniques, integrating deep mathematical concepts from a range of different fields, which will be of great interest to all researchers involved with image processing and computer vision applications, as well as computations for more general scientific problems.
Book Description
I. The past. the present . . . and the future It is possible to take the view that ever since it began, the "ancient" branch of physics known as Optics has been concerned with process ing images. But since the Nineteen-Thirties increasingly close ties have been forming between Optics, which until then had been largely based on instruments, and the sciences of communication and infor mation arising out of mathematics and electronics. Such developments follow naturally, since communication systems and image-forming systems are all designed to receive or transmit information. Further more the same mathematical forms are used for describing the beha viour of electrical and optical systems. It is a question of systems theory, particularly linear systems, and of Fourier's analysis methods, which together constitute an important part of Signal Theory. In the case of communication systems carrying signals of an electrical nature, information is time-related or temporal. Transmitted signals are one-dimensional and functions of a single variable, time t. In the case of optical systems information is spatial in nature. Signals are distributions of light intensity in space. In general they are treated as two-dimensional signals, being functions of two spatia! variables written as x and y. In the early Fifties the way forward became clearer still when some scientists at the Institut d'Optique in Paris began using optical filtering techniques in coherent light in order to enhance the quality of photographs.
Author: Ronald K. Pearson Publisher: CRC Press ISBN: 1498714137 Category : Medical Languages : en Pages : 286
Book Description
Nonlinear Digital Filtering with Python: An Introduction discusses important structural filter classes including the median filter and a number of its extensions (e.g., weighted and recursive median filters), and Volterra filters based on polynomial nonlinearities. Adopting both structural and behavioral approaches in characterizing and designing nonlinear digital filters, this book: Begins with an expedient introduction to programming in the free, open-source computing environment of Python Uses results from algebra and the theory of functional equations to construct and characterize behaviorally defined nonlinear filter classes Analyzes the impact of a range of useful interconnection strategies on filter behavior, providing Python implementations of the presented filters and interconnection strategies Proposes practical, bottom-up strategies for designing more complex and capable filters from simpler components in a way that preserves the key properties of these components Illustrates the behavioral consequences of allowing recursive (i.e., feedback) interconnections in nonlinear digital filters while highlighting a challenging but promising research frontier Nonlinear Digital Filtering with Python: An Introduction supplies essential knowledge useful for developing and implementing data cleaning filters for dynamic data analysis and time-series modeling.
Author: Edward R. Dougherty
Author: Edward R. Dougherty
Author: Sanjit Mitra
Author: Edward R. Dougherty
Author: Edward R. Dougherty
Author: Guy Gilboa
Author: André Marion
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Author: Ronald K. Pearson
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