Designing systolic architectures for complete Euclidean distance transform

Abstract

The Euclidean distance transform (EDT) converts a binary image into one where each pixel has a value equal to the Euclidean distance to the nearest foreground pixel. It has important uses in image analysis, computer vision and robotics, and so its VLSI implementation is very useful. In this paper, a sequential algorithm which does not require global operations is first presented. We then apply a sequence of algorithm transforms to convert it into one directly realizable as a square shaped systolic array, and a triangular shaped array after folding transformation. For an n x n image on an equal-sized systolic array, the computation consists of a forward scan and a reverse scan with O (n) time complexity. For images larger than the systolic array, an efficient partitioning and scheduling method based on array emulation is also given.