A parallel implementation of exact Euclidean distance transform based on exact dilations

Abstract This article reports the effective implementation of the exact Euclidean distance transform in a distributed system based on standard PCs, by using a simple data exchange protocol. The approach is based on the concept of exact distances, namely the denumerable set of distances found on the orthogonal lattice. The exact dilation algorithm, introduced recently, involves the successive scanning of the image elements for consecutive exact distance values, while assigning these values to empty neighboring pixels. The use of data compression methodology, as well as the quantitative characterization of the parallel efficiency, are also investigated and discussed considering several image sizes and the quantity of foreground elements. Among the obtained results, we have that the latter parameter strongly affects the overall performance and that the compressing strategy represents a potentially useful resource for increasing the overall processing efficiency.

[1]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Alexander Zelinsky,et al.  A mobile robot exploration algorithm , 1992, IEEE Trans. Robotics Autom..

[3]  O. Cuisenaire.  Distance transformations: fast algorithms and applications to medical image processing , 1999 .

[4]  Mats Björkman,et al.  Adaptive end-to-end compression for variable-bandwidth communication , 1999, Comput. Networks.

[5]  Weiguang Guan,et al.  A List-Processing Approach to Compute Voronoi Diagrams and the Euclidean Distance Transform , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Allan Gottlieb,et al.  Highly parallel computing , 1989, Benjamin/Cummings Series in computer science and engineering.

[7]  Manfredo P. do Carmo,et al.  Differential geometry of curves and surfaces , 1976 .

[8]  Ling Chen,et al.  An Efficient Algorithm for Complete Euclidean Distance Transform on Mesh-Connected SIMD , 1995, Parallel Comput..

[9]  Alfred M. Bruckstein,et al.  Skeletonization via Distance Maps and Level Sets , 1995, Comput. Vis. Image Underst..

[10]  Jaap van Pelt,et al.  A shape analysis framework for neuromorphometry , 2002, Network.

[11]  Jarmo Takala,et al.  Distance Transform Algorithm for Bit-Serial SIMD Architectures , 1999, Comput. Vis. Image Underst..

[12]  Fernand Meyer,et al.  Topographic distance and watershed lines , 1994, Signal Process..

[13]  Kenneth Falconer,et al.  Fractal Geometry: Mathematical Foundations and Applications , 1990 .

[14]  Shi-Jinn Horng,et al.  Parallel Computation of the Euclidean Distance Transform on the Mesh of Trees and the Hypercube Computer , 1997, Comput. Vis. Image Underst..