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A New Approach to Mathematical Morphology on One Dimensional Sampled Signals
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: 0000-0002-0612-558X
Flagship Biosci Inc, Westminster, CO USA.
Lulea Univ Technol, Lulea, Sweden.
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: 0000-0001-7764-1787
2016 (English)In: IEEE Proceedings, International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, 2016, 2016Conference paper, Published paper (Refereed)
Abstract [en]

We present a new approach to approximate continuous-domain mathematical morphology operators. The approach is applicable to irregularly sampled signals. We define a dilation under this new approach, where samples are duplicated and shifted according to the flat, continuous structuring element. We define the erosion by adjunction, and the opening and closing by composition. These new operators will significantly increase precision in image measurements. Experiments show that these operators indeed approximate continuous-domain operators better than the standard operators on sampled one-dimensional signals, and that they may be applied to signals using structuring elements smaller than the distance between samples. We also show that we can apply the operators to scan lines of a two-dimensional image to filter horizontal and vertical linear structures.

Place, publisher, year, edition, pages
2016.
National Category
Computer Sciences Computational Mathematics
Identifiers
URN: urn:nbn:se:uu:diva-309925DOI: 10.1109/ICPR.2016.7900244ISI: 000406771303148OAI: oai:DiVA.org:uu-309925DiVA, id: diva2:1053067
Conference
International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, 2016
Funder
Swedish Research Council, 2014-5983Available from: 2016-12-08 Created: 2016-12-08 Last updated: 2018-03-16Bibliographically approved

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Asplund, TeoStrand, Robin

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