Finding curvilinear features in speckled images
A method for finding curves in digital images with speckle noise is described. The solution method differs from standard linear convolutions followed by thresholds in that it explicitly allows curvature in the features. Maximum a posteriori (MAP) estimation is used, together with statistical models...
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ftnasantrs:oai:casi.ntrs.nasa.gov:19900062626 2023-05-15T18:18:16+02:00 Finding curvilinear features in speckled images Samadani, Ramin Vesecky, John F. Unclassified, Unlimited, Publicly available Jul 1, 1990 http://ntrs.nasa.gov/search.jsp?R=19900062626 unknown http://ntrs.nasa.gov/search.jsp?R=19900062626 Accession ID: 90A49681 Copyright Other Sources 63 Vancouver, Canada, July 10-14, 1989) IEEE Transactions on Geoscience and Remote Sensing; 669-673 1990 ftnasantrs 2012-02-15T18:35:22Z A method for finding curves in digital images with speckle noise is described. The solution method differs from standard linear convolutions followed by thresholds in that it explicitly allows curvature in the features. Maximum a posteriori (MAP) estimation is used, together with statistical models for the speckle noise and for the curve-generation process, to find the most probable estimate of the feature, given the image data. The estimation process is first described in general terms. Then, incorporation of the specific neighborhood system and a multiplicative noise model for speckle allows derivation of the solution, using dynamic programming, of the estimation problem. The detection of curvilinear features is considered separately. The detection results allow the determination of the minimal size of detectable feature. Finally, the estimation of linear features, followed by a detection step, is shown for computer-simulated images and for a SAR image of sea ice. Other/Unknown Material Sea ice NASA Technical Reports Server (NTRS) |
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NASA Technical Reports Server (NTRS) |
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ftnasantrs |
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63 Samadani, Ramin Vesecky, John F. Finding curvilinear features in speckled images |
topic_facet |
63 |
description |
A method for finding curves in digital images with speckle noise is described. The solution method differs from standard linear convolutions followed by thresholds in that it explicitly allows curvature in the features. Maximum a posteriori (MAP) estimation is used, together with statistical models for the speckle noise and for the curve-generation process, to find the most probable estimate of the feature, given the image data. The estimation process is first described in general terms. Then, incorporation of the specific neighborhood system and a multiplicative noise model for speckle allows derivation of the solution, using dynamic programming, of the estimation problem. The detection of curvilinear features is considered separately. The detection results allow the determination of the minimal size of detectable feature. Finally, the estimation of linear features, followed by a detection step, is shown for computer-simulated images and for a SAR image of sea ice. |
format |
Other/Unknown Material |
author |
Samadani, Ramin Vesecky, John F. |
author_facet |
Samadani, Ramin Vesecky, John F. |
author_sort |
Samadani, Ramin |
title |
Finding curvilinear features in speckled images |
title_short |
Finding curvilinear features in speckled images |
title_full |
Finding curvilinear features in speckled images |
title_fullStr |
Finding curvilinear features in speckled images |
title_full_unstemmed |
Finding curvilinear features in speckled images |
title_sort |
finding curvilinear features in speckled images |
publishDate |
1990 |
url |
http://ntrs.nasa.gov/search.jsp?R=19900062626 |
op_coverage |
Unclassified, Unlimited, Publicly available |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Other Sources |
op_relation |
http://ntrs.nasa.gov/search.jsp?R=19900062626 Accession ID: 90A49681 |
op_rights |
Copyright |
_version_ |
1766194783263391744 |