Euclidean distance transform for binary images on reconfigurable mesh-connected computers

The distance calculation in an image is a basic operation in computer vision, pattern recognition, and robotics. Several parallel algorithms have been proposed for calculating the Euclidean distance transform (EDT). Recently, Chen and Chuang proposed a parallel algorithm for computing the EDT on mesh-connected SIMD computers (1995). For an n/spl times/n image, their algorithm runs in O(n) time on a two-dimensional (2-D) n/spl times/n mesh-connected processor array. In this paper, we propose a more efficient parallel algorithm for computing the EDT on a reconfigurable mesh model. For the same problem, our algorithm runs in O(log /sup 2/n) time on a 2-D n/spl times/n reconfigurable mesh. Since a reconfigurable mesh uses the same amount of VLSI area as a plain mesh of the same size does when implemented in VLSI, our algorithm improves the result in [3] significantly.