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Exact evaluation of stochastic watersheds: From trees to general graphs
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
2014 (English)In: Discrete Geometry for Computer Imagery, Springer Berlin/Heidelberg, 2014, 309-319 p.Conference paper, Published paper (Refereed)
Abstract [en]

The stochastic watershed is a method for identifying salient contours in an image, with applications to image segmentation. The method computes a probability density function (PDF), assigning to each piece of contour in the image the probability to appear as a segmentation boundary in seeded watershed segmentation with randomly selected seedpoints. Contours that appear with high probability are assumed to be more important. This paper concerns an efficient method for computing the stochastic watershed PDF exactly, without performing any actual seeded watershed computations. A method for exact evaluation of stochastic watersheds was proposed by Meyer and Stawiaski (2010). Their method does not operate directly on the image, but on a compact tree representation where each edge in the tree corresponds to a watershed partition of the image elements. The output of the exact evaluation algorithm is thus a PDF defined over the edges of the tree. While the compact tree representation is useful in its own right, it is in many cases desirable to convert the results from this abstract representation back to the image, e. g, for further processing. Here, we present an efficient linear time algorithm for performing this conversion.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2014. 309-319 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 8668
Keyword [en]
stochastic watershed, watershed cut, minimum spanning tree
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:uu:diva-232251DOI: 10.1007/978-3-319-09955-2_26ISI: 000358195100026ISBN: 978-3-319-09954-5 (print)OAI: oai:DiVA.org:uu-232251DiVA: diva2:747199
Conference
18th IAPR International Conference on Discrete Geometry for Computer Imagery (DGCI), 2014, September 10-12, Siena, Italy
Available from: 2014-09-16 Created: 2014-09-16 Last updated: 2015-08-31Bibliographically approved

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Malmberg, FilipSelig, BettinaLuengo Hendriks, Cris L.

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