In 2003, Maurer at al. published a paper describing an algorithm that computes the exact distance transform in a linear time (with respect to image size) for the rectangular binary images in the k-dimensional space R^k and distance measured with respect to L_p-metric for 1 ≤ p ≤ &infin, which includes Euclidean distance L₂. In this paper we discuss this algorithm from theoretical and practical points of view. On the practical side, we concentrate on its Euclidean distance version, discuss the possible ways of implementing it as signed distance transform, and experimentally compare implemented algorithms. We also describe the parallelization of these algorithms and the computation time savings associated with such an implementation. The discussed implementations will be made available as a part of the CAVASS software system developed and maintained in our group. On the theoretical side, we prove that our version of the signed distance transform algorithm, GBDT, returns, in a linear time, the exact value of the distance from the geometrically defined object boundary. We notice that, actually, the precise form of the algorithm fromMaurer at al. is not well defined for L₁ and L_&infin metrics and point to our complete proof (not given in Maurer at al.) that all these algorithms work correctly for the L_p-metric with 1 < p < &infin.

Conference Proceeding reprint.

Full paper.

Last modified May 13, 2009.