TY - JOUR

T1 - Fast and scalable algorithms for the Euclidean distance transform on a linear array with a reconfigurable pipelined bus system

AU - Datta, Amitava

AU - Soundaralarshmi, S.

PY - 2004

Y1 - 2004

N2 - The Euclidean distance tranform (EDT) is an operation to convert a binary image consisting of black and white pixels to a representation where each pixel has the Euclidean distance of the nearest black pixel. The EDT has many applications in computer vision and image processing. We present two algorithms for computing the EDT on the linear array with a reconfigurable pipelined bus system (LARPBS), a recently proposed architecture based on optical buses. Our first algorithm runs in O(log log N) time for a binary N x N image on an LARPBS with N2+epsilon processors, for any fixed epsilon. 0<ε<1. Our second algorithm is highly scalable and runs in O(N-2/rho(2) log log p) time if the LARPBS has only p(2-epsilon) processors for p < N. The previous best deterministic algorithm for computing the EDT on the LARPBS is by Pan etal. Proceedings of IPDPS 2000 Workshop on Parallel and Distributed Computing in Image Processing, Video Processing and Multimedia (PDIVM 2000), Lecture Notes in Computer Science, Vol. 1800, Springer, Berlin, 178). Their algorithm runs either in 0(log N log log N) time on an LARPBS with N-2 processors or in O(log log N) time on an LARPBS with O(N-3) processors. (C) 2004 Elsevier Inc. All rights reserved.

AB - The Euclidean distance tranform (EDT) is an operation to convert a binary image consisting of black and white pixels to a representation where each pixel has the Euclidean distance of the nearest black pixel. The EDT has many applications in computer vision and image processing. We present two algorithms for computing the EDT on the linear array with a reconfigurable pipelined bus system (LARPBS), a recently proposed architecture based on optical buses. Our first algorithm runs in O(log log N) time for a binary N x N image on an LARPBS with N2+epsilon processors, for any fixed epsilon. 0<ε<1. Our second algorithm is highly scalable and runs in O(N-2/rho(2) log log p) time if the LARPBS has only p(2-epsilon) processors for p < N. The previous best deterministic algorithm for computing the EDT on the LARPBS is by Pan etal. Proceedings of IPDPS 2000 Workshop on Parallel and Distributed Computing in Image Processing, Video Processing and Multimedia (PDIVM 2000), Lecture Notes in Computer Science, Vol. 1800, Springer, Berlin, 178). Their algorithm runs either in 0(log N log log N) time on an LARPBS with N-2 processors or in O(log log N) time on an LARPBS with O(N-3) processors. (C) 2004 Elsevier Inc. All rights reserved.

U2 - 10.1016/j.jpdc.2004.01.002

DO - 10.1016/j.jpdc.2004.01.002

M3 - Article

SN - 0743-7315

VL - 64

SP - 360

EP - 369

JO - Journal of Parallel and Distributed Computing

JF - Journal of Parallel and Distributed Computing

IS - 3

ER -