Fast and scalable selection algorithms with applications to median filtering

Authors:
Chin-Hsiung Wu;Shi-Jinn Horng
Affiliations:
Dept. of Inf. Manage., Chinese Naval Acad., Kaohsiung, Taiwan;-
Venue:
IEEE Transactions on Parallel and Distributed Systems
Year:
2003

Citing 0
Cited 4

Efficient Algorithms for the All Nearest Neighbor and Closest Pair Problems on the Linear Array with a Reconfigurable Pipelined Bus System

IEEE Transactions on Parallel and Distributed Systems
Fast median and bilateral filtering

ACM SIGGRAPH 2006 Papers
Fast and scalable computations of 2D image moments

Image and Vision Computing
A parallel solution for high resolution histological image analysis

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

The main contributions of this paper are in designing fast and scalable parallel algorithms for selection and median filtering. Based on the radix-ω representation of data and the prune-and-search approach, we first design a fast and scalable selection algorithm on the arrays with reconfigurable optical buses (AROB). To the authors' knowledge, this is the most time efficient algorithm yet published, especially compared to the algorithms proposed by Han et al (2002) and Pan (1994). Then, given an N × N image and a W × W window, based on the proposed selection algorithm, several scalable median filtering algorithms are developed on the AROB model with a various number of processors. In the sense of the product of time and the number of processors used, most of the proposed algorithms are time or cost optimal.