Fuzzy Image Processing and Applications with MATLAB / Edition 1

Fuzzy Image Processing and Applications with MATLAB / Edition 1

by Tamalika Chaira, Ajoy Kumar Ray
     
 

ISBN-10: 1439807086

ISBN-13: 9781439807088

Pub. Date: 11/09/2009

Publisher: Taylor & Francis

In contrast to classical image analysis methods that employ "crisp" mathematics, fuzzy set techniques provide an elegant foundation and a set of rich methodologies for diverse image-processing tasks. However, a solid understanding of fuzzy processing requires a firm grasp of essential principles and background knowledge.

Fuzzy Image Processing and Applications

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Overview

In contrast to classical image analysis methods that employ "crisp" mathematics, fuzzy set techniques provide an elegant foundation and a set of rich methodologies for diverse image-processing tasks. However, a solid understanding of fuzzy processing requires a firm grasp of essential principles and background knowledge.

Fuzzy Image Processing and Applications with MATLAB® presents the integral science and essential mathematics behind this exciting and dynamic branch of image processing, which is becoming increasingly important to applications in areas such as remote sensing, medical imaging, and video surveillance, to name a few.

Many texts cover the use of crisp sets, but this book stands apart by exploring the explosion of interest and significant growth in fuzzy set image processing. The distinguished authors clearly lay out theoretical concepts and applications of fuzzy set theory and their impact on areas such as enhancement, segmentation, filtering, edge detection, content-based image retrieval, pattern recognition, and clustering. They describe all components of fuzzy, detailing preprocessing, threshold detection, and match-based segmentation.

Minimize Processing Errors Using Dynamic Fuzzy Set Theory

This book serves as a primer on MATLAB and demonstrates how to implement it in fuzzy image processing methods. It illustrates how the code can be used to improve calculations that help prevent or deal with imprecision—whether it is in the grey level of the image, geometry of an object, definition of an object’s edges or boundaries, or in knowledge representation, object recognition, or image interpretation.

The text addresses these considerations by applying fuzzy set theory to image thresholding, segmentation, edge detection, enhancement, clustering, color retrieval, clustering in pattern recognition, and other image processing operations. Highlighting key ideas, the authors present the experimental results of their own new fuzzy approaches and those suggested by different authors, offering data and insights that will be useful to teachers, scientists, and engineers, among others.

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Product Details

ISBN-13:
9781439807088
Publisher:
Taylor & Francis
Publication date:
11/09/2009
Pages:
237
Product dimensions:
6.30(w) x 9.30(h) x 0.70(d)

Table of Contents

Fuzzy Subsets and Operations

Concept of Fuzzy Subsets and Membership Function

Linguistic Hedges

Operations on Fuzzy Sets

Fuzzy Relations

Image Processing in an Imprecise Environment

Image as a Fuzzy Set

Fuzzy Image Processing

Some Applications of Fuzzy Set Theory in Image Processing

Fuzzy Similarity Measure, Measure of Fuzziness, and Entropy

Fuzzy Similarity and Distance Measures

Examples of Similarity Measures

Measures of Fuzziness

Fuzzy Entropy

Geometry of Fuzzy Subsets

Fuzzy Image Preprocessing

Contrast Enhancement

Fuzzy Image Contrast Enhancement

Filters

Fuzzy Filters

Thresholding Detection in Fuzzy Images

Threshold Detection Methods

Types of Thresholding

Thresholding Methods

Types of Fuzzy Methods

Application of Thresholding

Fuzzy Match-Based Region Extraction

Match-Based Region Extraction

Back Projection Algorithm

Fuzzy Region Extraction Methods

Fuzzy Edge Detection

Methods for Edge Detection

Fuzzy Methods

Fuzzy Content-Based Image Retrieval

Color Spaces

Content-Based Color Image Retrieval

An Image Retrieval Model

Fuzzy-Based Image Retrieval Methods

Fuzzy Methods in Pattern Classification

Decision Theoretic Pattern Classification Techniques

Why a Fuzzy Classifier

Fuzzy Set Theoretic Approach to Pattern Classification

Fuzzy Supervised Learning Algorithm

Fuzzy Partition

Fuzzy Unsupervised Pattern Classification

Application of Fuzzy Set Theory in Remote Sensing

Why Fuzzy Techniques in Remote Sensing

About the Remotely Sensed Data

Classification of Remotely Sensed Data

Fuzzy Sets in Remote Sensing Data Analysis

Background Work in Neuro Fuzzy Computing in Remote Sensing

Background Work on Fuzzy Sets in Remote Sensing

Segmentation of Remote Sensing Images

Fuzzy Multilayer Perceptron

Fuzzy Counter-Propagation Network (CPN)

Fuzzy CPN for Classification of Remotely Sensed Data

MATLAB Programs

MATLAB Examples

Problems

Index

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