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Distance Transform Based on Weight Sequences
Eastern Mediterranean University, Famagusta, North Cyprus, via Mersin-10, Turkey .
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´e de Nantes, LS2N UMR CNRS 6004, Nantes, France .
2019 (English)In: DGCI 2019: Discrete Geometry for Computer Imagery, Switzerland AG: Springer , 2019, Vol. 11414, p. 62-74Conference paper, Published paper (Refereed)
Abstract [en]

 There is a continuous effort to develop the theory and methods for computing digital distance functions, and to lower the rotational dependency of distance functions. Working on the digital space, e.g., on the square grid,digital distance functions are defined by minimal costpaths, which can be processed (back-tracked etc.) without any errors or approximations. Recently, digital distance functions defined by weight sequences, which is a concept allowing multiple types of weighted steps combined with neighborhood sequences, were developed. With appropriate weight sequences, the distance between points on the perimeter of a square and the center of the square (i.e., for squares of a given size the weight sequence can be easily computed) are exactly the Euclidean distance for these distances based on weight sequences. However, distances based on weight sequences may not fulfill the triangular inequality. In this paper, continuing the research, we provide a sufficient condition for weight sequences to provide metric distance. Further, we present an algorithm to compute the distance transform based on these distances. Optimization results are also shown for the approximation of the Euclidean distance inside the given square.

Place, publisher, year, edition, pages
Switzerland AG: Springer , 2019. Vol. 11414, p. 62-74
Series
Lecture Notes in Computer Science ; 11414
Keywords [en]
Digital distances · Weight sequences · Distance transforms · Neighborhood sequences · Chamfer distances · Combined distances · Approximation of the Euclidean distance
National Category
Discrete Mathematics
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-390560DOI: 10.1007/978-3-030-14085-4_6ISBN: 978-3-030-14084-7 (print)ISBN: 978-3-030-14085-4 (print)OAI: oai:DiVA.org:uu-390560DiVA, id: diva2:1342005
Conference
International Conference on Discrete Geometry for Computer Imagery
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-08-12

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CiteExportLink to record
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