Image Segmentation Using Binary Tree Structured Markov Random Fields
Synthetic Aperture Radar (SAR) systems are important active sensors for Earth observation systems. They enable continuous monitoring of the targets (e.g., sea ice, agricultural areas, ships, etc.) through clouds and independent of solar illumination. Space-borne polarimetric SAR missions which are p...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.136.141 2023-05-15T18:17:38+02:00 Image Segmentation Using Binary Tree Structured Markov Random Fields Kaan Ersahin The Pennsylvania State University CiteSeerX Archives 2004 application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.141 http://www.cs.ubc.ca/~murphyk/Teaching/CS532c_Fall04/Projects/erashin.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.141 http://www.cs.ubc.ca/~murphyk/Teaching/CS532c_Fall04/Projects/erashin.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www.cs.ubc.ca/~murphyk/Teaching/CS532c_Fall04/Projects/erashin.pdf text 2004 ftciteseerx 2016-01-07T14:43:15Z Synthetic Aperture Radar (SAR) systems are important active sensors for Earth observation systems. They enable continuous monitoring of the targets (e.g., sea ice, agricultural areas, ships, etc.) through clouds and independent of solar illumination. Space-borne polarimetric SAR missions which are planned for the near future draw our attention to the crucial task of information extraction from such data. The Text Sea ice Unknown |
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ftciteseerx |
language |
English |
description |
Synthetic Aperture Radar (SAR) systems are important active sensors for Earth observation systems. They enable continuous monitoring of the targets (e.g., sea ice, agricultural areas, ships, etc.) through clouds and independent of solar illumination. Space-borne polarimetric SAR missions which are planned for the near future draw our attention to the crucial task of information extraction from such data. The |
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The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Kaan Ersahin |
spellingShingle |
Kaan Ersahin Image Segmentation Using Binary Tree Structured Markov Random Fields |
author_facet |
Kaan Ersahin |
author_sort |
Kaan Ersahin |
title |
Image Segmentation Using Binary Tree Structured Markov Random Fields |
title_short |
Image Segmentation Using Binary Tree Structured Markov Random Fields |
title_full |
Image Segmentation Using Binary Tree Structured Markov Random Fields |
title_fullStr |
Image Segmentation Using Binary Tree Structured Markov Random Fields |
title_full_unstemmed |
Image Segmentation Using Binary Tree Structured Markov Random Fields |
title_sort |
image segmentation using binary tree structured markov random fields |
publishDate |
2004 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.141 http://www.cs.ubc.ca/~murphyk/Teaching/CS532c_Fall04/Projects/erashin.pdf |
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Sea ice |
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Sea ice |
op_source |
http://www.cs.ubc.ca/~murphyk/Teaching/CS532c_Fall04/Projects/erashin.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.141 http://www.cs.ubc.ca/~murphyk/Teaching/CS532c_Fall04/Projects/erashin.pdf |
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Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766192223877070848 |