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A Weight Sequence Distance Function
University of Debrecen, Department of Computer Science, Debrecen Hungary .ORCID iD: 0000-0002-9494-6440
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.
Universit ́ de Nantes, IRCCyN UMR CNRS 6597, Nantes, France.
2013 (English)In: : Mathematical Morphology and Its Applications to Signal and Image Processing / [ed] Cris L. Luengo Hendriks, Gunilla Borgefors, Robin Strand, Springer Berlin/Heidelberg, 2013, p. 292-301Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, a family of weighted neighborhood sequence distance functions defined on the square grid is presented. With this distance function, the allowed weight between any two adjacent pixels along a path is given by a weight sequence. We build on our previous results, where only two or three unique weights are considered, and present a framework that allows any number of weights. We show that the rotational dependency can be very low when as few as three or four unique weights are used. An algorithm for computing the distance transform (DT) that can be used for image processing applications is also presented.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2013. p. 292-301
Series
Lecture Notes in Computer Science ; 7883
National Category
Other Computer and Information Science
Research subject
Computerized Image Analysis; Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-210999DOI: 10.1007/978-3-642-38294-9_25OAI: oai:DiVA.org:uu-210999DiVA, id: diva2:665117
Conference
11th International Symposium on Mathematical Morphology, ISMM 2013, May 27-29 2013, Uppsala
Available from: 2013-11-19 Created: 2013-11-19 Last updated: 2018-01-11

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Benedek, NagyStrand, Robin

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