Rough Fuzzy Image Analysis: Foundations and Methodologies

by Sankar K. Pal (Editor), James F. Peters (Editor)

Overview

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

Product Details

ISBN-13:	9781040212325
Publisher:	CRC Press
Publication date:	05/04/2010
Series:	Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series
Sold by:	Barnes & Noble
Format:	eBook
Pages:	266
File size:	13 MB
Note:	This product may take a few minutes to download.

About the Author

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.

Table of Contents

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.