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Stochastic Distance Transform
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.ORCID iD: 0000-0003-0253-9037
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.ORCID iD: 0000-0001-7312-8222
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.ORCID iD: 0000-0002-6041-6310
2019 (English)In: Discrete Geometry for Computer Imagery, Springer, 2019, p. 75-86Conference paper, Published paper (Refereed)
Abstract [en]

The distance transform (DT) and its many variations are ubiquitous tools for image processing and analysis. In many imaging scenarios, the images of interest are corrupted by noise. This has a strong negative impact on the accuracy of the DT, which is highly sensitive to spurious noise points. In this study, we consider images represented as discrete random sets and observe statistics of DT computed on such representations. We, thus, define a stochastic distance transform (SDT), which has an adjustable robustness to noise. Both a stochastic Monte Carlo method and a deterministic method for computing the SDT are proposed and compared. Through a series of empirical tests, we demonstrate that the SDT is effective not only in improving the accuracy of the computed distances in the presence of noise, but also in improving the performance of template matching and watershed segmentation of partially overlapping objects, which are examples of typical applications where DTs are utilized.

Place, publisher, year, edition, pages
Springer, 2019. p. 75-86
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 11414
Keywords [en]
distance transform, stochastic, robustness to noise, random sets, monte carlo, template matching, watershed segmentation
National Category
Computer Vision and Robotics (Autonomous Systems) Discrete Mathematics
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-381027DOI: 10.1007/978-3-030-14085-4_7OAI: oai:DiVA.org:uu-381027DiVA, id: diva2:1301884
Conference
21th International Conference on Discrete Geometry for Computer Imagery
Available from: 2019-02-23 Created: 2019-04-03 Last updated: 2019-04-03

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Öfverstedt, JohanLindblad, JoakimSladoje, Natasa

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Computer Vision and Robotics (Autonomous Systems)Discrete Mathematics

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