Rough Fuzzy Image Analysis
Foundations and Methodologies
1st Edition
Published on 4. May 2010
266 pages
978-1-4398-0330-1 (ISBN)
Description
Fuzzy sets, near sets, and rough sets are useful and important stepping stones in a variety of approaches to image analysis. These three types of sets and their various hybridizations provide powerful frameworks for image analysis. Emphasizing the utility of fuzzy, near, and rough sets in image analysis, Rough Fuzzy Image Analysis: Foundations and
More details
Series
Edition
1. Auflage
Language
English
Place of publication
Baton Rouge
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Researchers and graduate students in computer science, mathematics, and electrical engineering.
Illustrations
41 s/w Tabellen, 113 s/w Abbildungen
113 b/w images, 41 tables and 500+
ISBN-13
978-1-4398-0330-1 (9781439803301)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
10/2017
1st Edition
CRC Press
€117.79
Shipment within 10-20 days
Book
05/2010
1st Edition
CRC Press
€272.36
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Persons
Sankar K. Pal is the director and a distinguished scientist of the Indian Statistical Institute in Kolkata.
James F. Peters is a professor in the Department of Electrical and Computer Engineering and group leader of the Computational Intelligence Laboratory at the University of Manitoba in Winnipeg, Canada.
James F. Peters is a professor in the Department of Electrical and Computer Engineering and group leader of the Computational Intelligence Laboratory at the University of Manitoba in Winnipeg, Canada.
Content
Cantor, Fuzzy, Near, and Rough Sets in Image Analysis. Rough Fuzzy Clustering Algorithm for Segmentation of Brain MR Images. Image Thresholding Using Generalized Rough Sets. Mathematical Morphology and Rough Sets. Rough Hybrid Scheme: An Application of Breast Cancer Imaging. Applications of Fuzzy Rule-Based Systems in Medical Image Understanding. Near Set Evaluation and Recognition (NEAR) System. Perceptual Systems Approach to Measuring Image Resemblance. From Tolerance Near Sets to Perceptual Image Analysis. Image Segmentation: A Rough-Set Theoretic Approach. Rough Fuzzy Measures in Image Segmentation and Analysis. Discovering Image Similarities: Tolerance Near Set Approach.
