Edge detection and image segmentation: Two sides of the same coin

Victor Boskovitz; Hugo Guterman

Edge detection and image segmentation: Two sides of the same coin

Victor Boskovitz, Hugo Guterman

Department of Electrical & Computer Engineering

Research output: Contribution to conference › Paper › peer-review

1 Scopus citations

Abstract

An auto-adaptive Neuro-Fuzzy segmentation and edge detection architecture is presented. The system consists of a multilayer perceptron (MLP) network that performs image segmentation by adaptive thresholding of the input image using labels automatically preselected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsupervised. The output status of the network is described as a fuzzy set. Fuzzy entropy is used as a measure of the error of the segmentation system as well as a criterion for determining potential edge pixels.

Original language	English
Pages	1063-1068
Number of pages	6
State	Published - 1 Jan 1997
Event	Proceedings of the 1997 6th IEEE International Conference on Fussy Systems, FUZZ-IEEE'97. Part 1 (of 3) - Barcelona, Spain Duration: 1 Jul 1997 → 5 Jul 1997

Conference

Conference	Proceedings of the 1997 6th IEEE International Conference on Fussy Systems, FUZZ-IEEE'97. Part 1 (of 3)
City	Barcelona, Spain
Period	1/07/97 → 5/07/97

ASJC Scopus subject areas

Software
Theoretical Computer Science
Artificial Intelligence
Applied Mathematics

Cite this

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title = "Edge detection and image segmentation: Two sides of the same coin",

abstract = "An auto-adaptive Neuro-Fuzzy segmentation and edge detection architecture is presented. The system consists of a multilayer perceptron (MLP) network that performs image segmentation by adaptive thresholding of the input image using labels automatically preselected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsupervised. The output status of the network is described as a fuzzy set. Fuzzy entropy is used as a measure of the error of the segmentation system as well as a criterion for determining potential edge pixels.",

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N2 - An auto-adaptive Neuro-Fuzzy segmentation and edge detection architecture is presented. The system consists of a multilayer perceptron (MLP) network that performs image segmentation by adaptive thresholding of the input image using labels automatically preselected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsupervised. The output status of the network is described as a fuzzy set. Fuzzy entropy is used as a measure of the error of the segmentation system as well as a criterion for determining potential edge pixels.

AB - An auto-adaptive Neuro-Fuzzy segmentation and edge detection architecture is presented. The system consists of a multilayer perceptron (MLP) network that performs image segmentation by adaptive thresholding of the input image using labels automatically preselected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsupervised. The output status of the network is described as a fuzzy set. Fuzzy entropy is used as a measure of the error of the segmentation system as well as a criterion for determining potential edge pixels.

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M3 - Paper

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ER -

Edge detection and image segmentation: Two sides of the same coin

Abstract

Conference

ASJC Scopus subject areas

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