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A general algorithm for computing distance transforms in linear time

A. Meijster, J. B. T. M. Roerdink, W. H. Hesselink

edited by: John Goutsias, Luc Vincent, DanS Bloomberg

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∞ norm) transforms.


Mathematical Morphology and its Applications to Image and Signal Processing In Mathematical Morphology and its Applications to Image and Signal Processing, Vol. 18 (2000), pp. 331-340, https://doi.org/10.1007/0-306-47025-x_36 
Key: INRMM:3759792

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