In Goutsias, J., Vincent, L., & Bloomberg, D., editors, *Mathematical Morphology and Its Applications to Image and Signal Processing*, volume 18, of *Computational Imaging and Vision*, pages 331–340. Springer US, 2000.

doi abstract bibtex

doi abstract bibtex

A new general algorithm for computing distance transforms of digital images is presented. The algorithm consists of two phases. Both phases consist of two scans, a forward and a backward scan. The first phase scans the image column-wise, while the second phase scans the image row-wise. Since the computation per row (column) is independent of the computation of other rows (columns), the algorithm can be easily parallelized on shared memory computers. The algorithm can be used for the computation of the exact Euclidean, Manhattan (L1 norm), and chessboard distance (L$\infty$ norm) transforms.

@incollection{meijsterGeneralAlgorithmComputing2000, title = {A General Algorithm for Computing Distance Transforms in Linear Time}, booktitle = {Mathematical {{Morphology}} and Its {{Applications}} to {{Image}} and {{Signal Processing}}}, author = {Meijster, A. and Roerdink, J. B. T. M. and Hesselink, W. H.}, editor = {Goutsias, John and Vincent, Luc and Bloomberg, DanS}, year = {2000}, volume = {18}, pages = {331--340}, publisher = {{Springer US}}, doi = {10.1007/0-306-47025-x\\_36}, abstract = {A new general algorithm for computing distance transforms of digital images is presented. The algorithm consists of two phases. Both phases consist of two scans, a forward and a backward scan. The first phase scans the image column-wise, while the second phase scans the image row-wise. Since the computation per row (column) is independent of the computation of other rows (columns), the algorithm can be easily parallelized on shared memory computers. The algorithm can be used for the computation of the exact Euclidean, Manhattan (L1 norm), and chessboard distance (L{$\infty$} norm) transforms.}, keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-3759792,algorithms,computational-science,data-transformation-modelling,distance-analysis,gridded-data}, lccn = {INRMM-MiD:c-3759792}, series = {Computational {{Imaging}} and {{Vision}}} }

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