A segmentation algorithm for noisy images

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This paper presents a 2-D image segmentation algorithm and addresses issues related to its performance on noisy images. The algorithm segments an image by first constructing a minimum spanning tree representation of the image and then partitioning the spanning tree into sub-trees representing different homogeneous regions. The spanning tree is partitioned in such a way that the sum of gray-level variations over all partitioned subtrees is minimized under the constraints that each subtree has at least a specified number of pixels and two adjacent subtrees have significantly different ``average`` gray-levels. Two types of noise, transmission errors and Gaussian additive noise. ... continued below

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7 p.

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Xu, Y.; Olman, V. & Uberbacher, E.C. December 31, 1996.

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Description

This paper presents a 2-D image segmentation algorithm and addresses issues related to its performance on noisy images. The algorithm segments an image by first constructing a minimum spanning tree representation of the image and then partitioning the spanning tree into sub-trees representing different homogeneous regions. The spanning tree is partitioned in such a way that the sum of gray-level variations over all partitioned subtrees is minimized under the constraints that each subtree has at least a specified number of pixels and two adjacent subtrees have significantly different ``average`` gray-levels. Two types of noise, transmission errors and Gaussian additive noise. are considered and their effects on the segmentation algorithm are studied. Evaluation results have shown that the segmentation algorithm is robust in the presence of these two types of noise.

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7 p.

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OSTI as DE96014695

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  • IEEE symposium on image, speech, natural language systems, Washington, DC (United States), 4-6 Nov 1996

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  • Other: DE96014695
  • Report No.: CONF-9611123--1
  • Grant Number: AC05-96OR22464
  • Office of Scientific & Technical Information Report Number: 437768
  • Archival Resource Key: ark:/67531/metadc682003

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Office of Scientific & Technical Information Technical Reports

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  • December 31, 1996

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  • July 25, 2015, 2:20 a.m.

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  • Jan. 21, 2016, 12:07 p.m.

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Xu, Y.; Olman, V. & Uberbacher, E.C. A segmentation algorithm for noisy images, article, December 31, 1996; Tennessee. (digital.library.unt.edu/ark:/67531/metadc682003/: accessed April 27, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.