High-Resolution Algorithm for Image Segmentation in the Presence of Correlated Noise

Multiple line characterization is a most common issue in image processing. A specific formalism turns the contour detection issue of image processing into a source localization issue of array processing. However, the existing methods do not address correlated noise. As a result, the detection perfor...

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Автори: Haiping Jiang, Salah Bourennane, Caroline Fossati
Формат: Стаття
Мова:English
Опубліковано: Hindawi Limited 2010-01-01
Серія:Journal of Electrical and Computer Engineering
Онлайн доступ:http://dx.doi.org/10.1155/2010/630768
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author Haiping Jiang
Salah Bourennane
Caroline Fossati
author_facet Haiping Jiang
Salah Bourennane
Caroline Fossati
author_sort Haiping Jiang
collection DOAJ
description Multiple line characterization is a most common issue in image processing. A specific formalism turns the contour detection issue of image processing into a source localization issue of array processing. However, the existing methods do not address correlated noise. As a result, the detection performance is degraded. In this paper, we propose to improve the subspace-based high-resolution methods by computing the fourth-order slice cumulant matrix of the received signals instead of second-order statistics, and we estimate contour parameters out of images impaired with correlated Gaussian noise. Simulation results are presented and show that the proposed methods improve line characterization performance compared to second-order statistics.
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spelling doaj.art-88bbb213e2894b66aac6aa1ef1bf22772024-10-03T07:07:14ZengHindawi LimitedJournal of Electrical and Computer Engineering2090-01472090-01552010-01-01201010.1155/2010/630768630768High-Resolution Algorithm for Image Segmentation in the Presence of Correlated NoiseHaiping Jiang0Salah Bourennane1Caroline Fossati2Institut Fresnel/UMR CNRS 6133, Ecole Centrale Marseille, D.U. de Saint-Jérôme, 13397 Marseille Cédex 20, FranceInstitut Fresnel/UMR CNRS 6133, Ecole Centrale Marseille, D.U. de Saint-Jérôme, 13397 Marseille Cédex 20, FranceInstitut Fresnel/UMR CNRS 6133, Ecole Centrale Marseille, D.U. de Saint-Jérôme, 13397 Marseille Cédex 20, FranceMultiple line characterization is a most common issue in image processing. A specific formalism turns the contour detection issue of image processing into a source localization issue of array processing. However, the existing methods do not address correlated noise. As a result, the detection performance is degraded. In this paper, we propose to improve the subspace-based high-resolution methods by computing the fourth-order slice cumulant matrix of the received signals instead of second-order statistics, and we estimate contour parameters out of images impaired with correlated Gaussian noise. Simulation results are presented and show that the proposed methods improve line characterization performance compared to second-order statistics.http://dx.doi.org/10.1155/2010/630768
spellingShingle Haiping Jiang
Salah Bourennane
Caroline Fossati
High-Resolution Algorithm for Image Segmentation in the Presence of Correlated Noise
Journal of Electrical and Computer Engineering
title High-Resolution Algorithm for Image Segmentation in the Presence of Correlated Noise
title_full High-Resolution Algorithm for Image Segmentation in the Presence of Correlated Noise
title_fullStr High-Resolution Algorithm for Image Segmentation in the Presence of Correlated Noise
title_full_unstemmed High-Resolution Algorithm for Image Segmentation in the Presence of Correlated Noise
title_short High-Resolution Algorithm for Image Segmentation in the Presence of Correlated Noise
title_sort high resolution algorithm for image segmentation in the presence of correlated noise
url http://dx.doi.org/10.1155/2010/630768
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AT salahbourennane highresolutionalgorithmforimagesegmentationinthepresenceofcorrelatednoise
AT carolinefossati highresolutionalgorithmforimagesegmentationinthepresenceofcorrelatednoise