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Path-Based Distance with Varying Weights and Neighborhood Sequences

Identifieur interne : 006356 ( Main/Exploration ); précédent : 006355; suivant : 006357

Path-Based Distance with Varying Weights and Neighborhood Sequences

Auteurs : Nicolas Normand [France, Australie] ; Robin Strand [Suède] ; Pierre Evenou [France] ; Aurore Arlicot [France]

Source :

RBID : ISTEX:A5F79A9A2C391DEFF2AF8806F2F3DC5B99B0E7B5

English descriptors

Abstract

Abstract: This paper presents a path-based distance where local displacement costs vary both according to the displacement vector and with the travelled distance. The corresponding distance transform algorithm is similar in its form to classical propagation-based algorithms, but the more variable distance increments are either stored in look-up-tables or computed on-the-fly. These distances and distance transform extend neighborhood-sequence distances, chamfer distances and generalized distances based on Minkowski sums. We introduce algorithms to compute, in $\mathbb Z^2$ , a translated version of a neighborhood sequence distance map with a limited number of neighbors, both for periodic and aperiodic sequences. A method to recover the centered distance map from the translated one is also introduced. Overall, the distance transform can be computed with minimal delay, without the need to wait for the whole input image before beginning to provide the result image.

Url:
DOI: 10.1007/978-3-642-19867-0_17


Affiliations:


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Le document en format XML

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<div type="abstract" xml:lang="en">Abstract: This paper presents a path-based distance where local displacement costs vary both according to the displacement vector and with the travelled distance. The corresponding distance transform algorithm is similar in its form to classical propagation-based algorithms, but the more variable distance increments are either stored in look-up-tables or computed on-the-fly. These distances and distance transform extend neighborhood-sequence distances, chamfer distances and generalized distances based on Minkowski sums. We introduce algorithms to compute, in $\mathbb Z^2$ , a translated version of a neighborhood sequence distance map with a limited number of neighbors, both for periodic and aperiodic sequences. A method to recover the centered distance map from the translated one is also introduced. Overall, the distance transform can be computed with minimal delay, without the need to wait for the whole input image before beginning to provide the result image.</div>
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