Overview

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).

Full Product Details

Author:   Edward R. Dougherty ,  Jaakko Astola
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 1993
Dimensions:   Width: 21.00cm , Height: 1.00cm , Length: 28.00cm
Weight:   0.466kg
ISBN:  

9781461363781

ISBN 10:   1461363780
Pages:   176
Publication Date:   22 December 2012
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Statistical Properties, Fixed Points, and Decomposition with WMMR Filters.- Asymptotic Behavior of Morphological Filters.- Nonlinear Filtering Structure for Image Smoothing in Mixed-Noise Environments.- Root-Signal Sets of Morphological Filters and their Use in Variable-Length BTC Image Coding.- Unification of Nonlinear Filtering in the Context of Binary Logical Calculus, Part I: Binary Filters.- Unification of Nonlinear Filtering in the Context of Binary Logical Calculus, Part II: Gray-Scale Filters.- Morphological Analysis of Discrete Random Shapes.- Inverse Problems for Granulometries by Erosion.- Design of a Multitask Neurovision Processor.- Wilson-Cowan Neural-Network Model in Image Processing.- Clustering Properties of Hierarchical Self-Organizing Maps.

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