Fuzzy edge detector using entropy optimization

Madasu Hanmandlu; John See; Shantaram Vasikarla

doi:10.1109/itcc.2004.1286542

Fuzzy edge detector using entropy optimization

Madasu Hanmandlu^*, John See, Shantaram Vasikarla

^*Corresponding author for this work

School of Mathematical & Computer Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

29 Citations (Scopus)

Abstract

This paper proposes a fuzzy-based approach to edge detection in gray-level images. The proposed fuzzy edge detector involves two phases - global contrast intensification and local fuzzy edge detection. In the first phase, a modified Gaussian membership function is chosen to represent each pixel in the fuzzy plane. A global contrast intensification operator, containing three parameters, viz., intensification parameter t, fuzzifier f_h and the crossover point x_c, is used to enhance the image. The entropy function is optimized to obtain the parameters f_h and x_c using the gradient descent function before applying the local edge operator in the second phase. The local edge operator is a generalized Gaussian function containing two exponential parameters, & alpha; and & beta;. These parameters are obtained by the similar entropy optimization method. By using the proposed technique, a marked visible improvement in the important edges is observed on various test images over common edge detectors.

Original language	English
Title of host publication	International Conferen ON Information Technology 2004
Subtitle of host publication	Coding Computing
Publisher	IEEE
Pages	665-670
Number of pages	6
ISBN (Print)	0769521088
DOIs	https://doi.org/10.1109/itcc.2004.1286542
Publication status	Published - 24 Aug 2004
Event	2004 International Conference on Information Technology: Coding Computing - Las Vegas, NV, United States Duration: 5 Apr 2004 → 7 Apr 2004

Conference

Conference	2004 International Conference on Information Technology: Coding Computing
Abbreviated title	ITCC 2004
Country/Territory	United States
City	Las Vegas, NV
Period	5/04/04 → 7/04/04

Keywords

Contrast intensification operator
Crossover point
Edge detector
Entropy
Fuzzifier
Fuzzy image processing
Gaussian membership function
Image enhancement

ASJC Scopus subject areas

Software
Information Systems
Engineering(all)

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10.1109/itcc.2004.1286542

Cite this

@inproceedings{42d36b83984249c185cc76ae207d2ad1,

title = "Fuzzy edge detector using entropy optimization",

abstract = "This paper proposes a fuzzy-based approach to edge detection in gray-level images. The proposed fuzzy edge detector involves two phases - global contrast intensification and local fuzzy edge detection. In the first phase, a modified Gaussian membership function is chosen to represent each pixel in the fuzzy plane. A global contrast intensification operator, containing three parameters, viz., intensification parameter t, fuzzifier fh and the crossover point xc, is used to enhance the image. The entropy function is optimized to obtain the parameters fh and xc using the gradient descent function before applying the local edge operator in the second phase. The local edge operator is a generalized Gaussian function containing two exponential parameters, & alpha; and & beta;. These parameters are obtained by the similar entropy optimization method. By using the proposed technique, a marked visible improvement in the important edges is observed on various test images over common edge detectors.",

keywords = "Contrast intensification operator, Crossover point, Edge detector, Entropy, Fuzzifier, Fuzzy image processing, Gaussian membership function, Image enhancement",

author = "Madasu Hanmandlu and John See and Shantaram Vasikarla",

note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2004 International Conference on Information Technology: Coding Computing, ITCC 2004 ; Conference date: 05-04-2004 Through 07-04-2004",

year = "2004",

month = aug,

day = "24",

doi = "10.1109/itcc.2004.1286542",

language = "English",

isbn = "0769521088",

pages = "665--670",

booktitle = "International Conferen ON Information Technology 2004",

publisher = "IEEE",

address = "United States",

}

TY - GEN

T1 - Fuzzy edge detector using entropy optimization

AU - Hanmandlu, Madasu

AU - See, John

AU - Vasikarla, Shantaram

PY - 2004/8/24

Y1 - 2004/8/24

N2 - This paper proposes a fuzzy-based approach to edge detection in gray-level images. The proposed fuzzy edge detector involves two phases - global contrast intensification and local fuzzy edge detection. In the first phase, a modified Gaussian membership function is chosen to represent each pixel in the fuzzy plane. A global contrast intensification operator, containing three parameters, viz., intensification parameter t, fuzzifier fh and the crossover point xc, is used to enhance the image. The entropy function is optimized to obtain the parameters fh and xc using the gradient descent function before applying the local edge operator in the second phase. The local edge operator is a generalized Gaussian function containing two exponential parameters, & alpha; and & beta;. These parameters are obtained by the similar entropy optimization method. By using the proposed technique, a marked visible improvement in the important edges is observed on various test images over common edge detectors.

AB - This paper proposes a fuzzy-based approach to edge detection in gray-level images. The proposed fuzzy edge detector involves two phases - global contrast intensification and local fuzzy edge detection. In the first phase, a modified Gaussian membership function is chosen to represent each pixel in the fuzzy plane. A global contrast intensification operator, containing three parameters, viz., intensification parameter t, fuzzifier fh and the crossover point xc, is used to enhance the image. The entropy function is optimized to obtain the parameters fh and xc using the gradient descent function before applying the local edge operator in the second phase. The local edge operator is a generalized Gaussian function containing two exponential parameters, & alpha; and & beta;. These parameters are obtained by the similar entropy optimization method. By using the proposed technique, a marked visible improvement in the important edges is observed on various test images over common edge detectors.

KW - Contrast intensification operator

KW - Crossover point

KW - Edge detector

KW - Entropy

KW - Fuzzifier

KW - Fuzzy image processing

KW - Gaussian membership function

KW - Image enhancement

UR - http://www.scopus.com/inward/record.url?scp=3042639102&partnerID=8YFLogxK

U2 - 10.1109/itcc.2004.1286542

DO - 10.1109/itcc.2004.1286542

M3 - Conference contribution

AN - SCOPUS:3042639102

SN - 0769521088

SP - 665

EP - 670

BT - International Conferen ON Information Technology 2004

PB - IEEE

T2 - 2004 International Conference on Information Technology: Coding Computing

Y2 - 5 April 2004 through 7 April 2004

ER -

Fuzzy edge detector using entropy optimization

Abstract

Conference

Keywords

ASJC Scopus subject areas

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