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Up to now, all the known Euclidean distance function algorithms are either excessively slow or inaccurate, and even Danielsson's method produces errors in some con gurations. We s h o w that they are due to the local way distances are propagated in images by this algorithm. To remedy these drawbacks, an algorithm is introduced, which encodes the objects boundaries as chains and propagates these structures in the image using rewriting rules. The chains convey Euclidean distances and can bedoi:10.1109/cvpr.1991.139746 dblp:conf/cvpr/Vincent91 fatcat:qwihcktf6ne3rdx7yfer4qcbme