Efficient Distance Transformation for Path-based Metrics

David Coeurjolly 1 Isabelle Sivignon 2
1 M2DisCo - Geometry Processing and Constrained Optimization
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 GIPSA-AGPIG - AGPIG
GIPSA-DIS - Département Images et Signal
Abstract : In many applications, separable algorithms have demonstrated their efficiency to perform high performance volumetric processing of shape, such as distance transformation or medial axis extraction. In the literature, several authors have discussed about conditions on the metric to be considered in a separable approach. In this article, we present generic separable algorithms to efficiently compute Voronoi maps and distance transformations for a large class of metrics. Focusing on path-based norms (chamfer masks, neighborhood sequences...), we propose efficient algorithms to compute such volumetric transformation in dimension $n$. We describe a new $O(n\cdot N^n\cdot\log{N}\cdot(n+\log f))$ algorithm for shapes in a $N^n$ domain for chamfer norms with a rational ball of $f$ facets (compared to $O(f^{\lfloor\frac{n}{2}\rfloor}\cdot N^n)$ with previous approaches). Last we further investigate an even more elaborate algorithm with the same worst-case complexity, but reaching a complexity of $O(n\cdot N^n\cdot\log{f}\cdot(n+\log f))$ experimentally, under assumption of regularity distribution of the mask vectors.
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https://hal.archives-ouvertes.fr/hal-02000339
Contributor : David Coeurjolly <>
Submitted on : Thursday, January 31, 2019 - 7:49:03 PM
Last modification on : Thursday, February 7, 2019 - 3:51:05 PM

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David Coeurjolly, Isabelle Sivignon. Efficient Distance Transformation for Path-based Metrics. 2019. ⟨hal-02000339⟩

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