Euclidean distance transform on Polymorphic Processor Array

Abstract

Describes a new parallel algorithm for the Euclidean distance transform on the Polymorphic Processor Array, a massively parallel architecture based on a reconfigurable mesh interconnection network. The transform converts a binary image which consists of object pixels and non-object pixels into an image where every pixel takes the value of the distance between itself and the nearest object pixel in the original image. The proposed algorithm has been implemented using the Polymorphic Parallel C language and has been validated through simulation. Its computational complexity is O(N) (worst case) for pictures of N/spl times/N pixels on a Polymorphic Processor Array of N/spl times/N processing elements.