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

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3 Author(s)
Pan, Y. ; Dept. of Comput. Sci., Dayton Univ., OH, USA ; Hamdi, M. ; Keqin Li

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×n image, their algorithm runs in O(n) time on a two-dimensional (2-D) n×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 2n) time on a 2-D n×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

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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:30 ,  Issue: 1 )