Dougherty

Mathematical Nonlinear Image Processing

A Special Issue of the Journal of Mathematical Imaging and Vision
Herausgegeben:Dougherty, Edward R.; Astola, Jaakko T.

• Gebundenes Buch

Produktbeschreibung

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).

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Produktdetails

• Verlag: Springer / Springer US / Springer, Berlin
• Artikelnr. des Verlages: 978-0-7923-9314-6
• Reprinted from JOURNAL OF MATHEMATICAL IMAGING AND VISION, 2:2/3, 1993
• Seitenzahl: 176
• Erscheinungstermin: 31. Dezember 1992
• Englisch
• Abmessung: 254mm x 178mm x 13mm
• Gewicht: 572g
• ISBN-13: 9780792393146
• ISBN-10: 0792393147
• Artikelnr.: 25009844

Inhaltsangabe

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.