Accuracy of edge detection methods with local information in speckled imagery

We compare the accuracy of five approaches for contour detection in speckled imagery. Some of these methods take advantage of the statistical properties of speckled data, and all of them employ active contours using B-spline curves. Images obtained with coherent illumination are affected by a noise...

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Detalles Bibliográficos
Autores principales: Gambini, María Juliana, Mejail, Marta Estela, Jacobo Berlles, Julio C. A.
Publicado: 2008
Materias:
Active contours
B-spline curve fitting
Image analysis
SAR imagery
Speckle noise
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09603174_v18_n1_p15_Gambini
http://hdl.handle.net/20.500.12110/paper_09603174_v18_n1_p15_Gambini
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_09603174_v18_n1_p15_Gambini
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spelling paper:paper_09603174_v18_n1_p15_Gambini2023-06-08T15:57:40Z Accuracy of edge detection methods with local information in speckled imagery Gambini, María Juliana Mejail, Marta Estela Jacobo Berlles, Julio C. A. Active contours B-spline curve fitting Image analysis SAR imagery Speckle noise We compare the accuracy of five approaches for contour detection in speckled imagery. Some of these methods take advantage of the statistical properties of speckled data, and all of them employ active contours using B-spline curves. Images obtained with coherent illumination are affected by a noise called speckle, which is inherent to the imaging process. These data have been statistically modeled by a multiplicative model using the G0 distribution, under which regions with different degrees of roughness can be characterized by the value of a parameter. We use this information to find boundaries between regions with different textures. We propose and compare five strategies for boundary detection: three based on the data (maximum discontinuity on raw data, fractal dimension and maximum likelihood) and two based on estimates of the roughness parameter (maximum discontinuity and anisotropic smoothed roughness estimates). In order to compare these strategies, a Monte Carlo experience was performed to assess the accuracy of fitting a curve to a region. The probability of finding the correct edge with less than a specified error is estimated and used to compare the techniques. The two best procedures are then compared in terms of their computational cost and, finally, we show that the maximum likelihood approach on the raw data using the G0 law is the best technique. © 2007 Springer Science+Business Media, LLC. Fil:Gambini, J. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Mejail, M.E. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Jacobo-Berlles, J. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2008 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09603174_v18_n1_p15_Gambini http://hdl.handle.net/20.500.12110/paper_09603174_v18_n1_p15_Gambini
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Active contours
B-spline curve fitting
Image analysis
SAR imagery
Speckle noise
spellingShingle Active contours
B-spline curve fitting
Image analysis
SAR imagery
Speckle noise
Gambini, María Juliana
Mejail, Marta Estela
Jacobo Berlles, Julio C. A.
Accuracy of edge detection methods with local information in speckled imagery
topic_facet Active contours
B-spline curve fitting
Image analysis
SAR imagery
Speckle noise
description We compare the accuracy of five approaches for contour detection in speckled imagery. Some of these methods take advantage of the statistical properties of speckled data, and all of them employ active contours using B-spline curves. Images obtained with coherent illumination are affected by a noise called speckle, which is inherent to the imaging process. These data have been statistically modeled by a multiplicative model using the G0 distribution, under which regions with different degrees of roughness can be characterized by the value of a parameter. We use this information to find boundaries between regions with different textures. We propose and compare five strategies for boundary detection: three based on the data (maximum discontinuity on raw data, fractal dimension and maximum likelihood) and two based on estimates of the roughness parameter (maximum discontinuity and anisotropic smoothed roughness estimates). In order to compare these strategies, a Monte Carlo experience was performed to assess the accuracy of fitting a curve to a region. The probability of finding the correct edge with less than a specified error is estimated and used to compare the techniques. The two best procedures are then compared in terms of their computational cost and, finally, we show that the maximum likelihood approach on the raw data using the G0 law is the best technique. © 2007 Springer Science+Business Media, LLC.
author Gambini, María Juliana
Mejail, Marta Estela
Jacobo Berlles, Julio C. A.
author_facet Gambini, María Juliana
Mejail, Marta Estela
Jacobo Berlles, Julio C. A.
author_sort Gambini, María Juliana
title Accuracy of edge detection methods with local information in speckled imagery
title_short Accuracy of edge detection methods with local information in speckled imagery
title_full Accuracy of edge detection methods with local information in speckled imagery
title_fullStr Accuracy of edge detection methods with local information in speckled imagery
title_full_unstemmed Accuracy of edge detection methods with local information in speckled imagery
title_sort accuracy of edge detection methods with local information in speckled imagery
publishDate 2008
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09603174_v18_n1_p15_Gambini
http://hdl.handle.net/20.500.12110/paper_09603174_v18_n1_p15_Gambini
work_keys_str_mv AT gambinimariajuliana accuracyofedgedetectionmethodswithlocalinformationinspeckledimagery
AT mejailmartaestela accuracyofedgedetectionmethodswithlocalinformationinspeckledimagery
AT jacoboberllesjulioca accuracyofedgedetectionmethodswithlocalinformationinspeckledimagery
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