Share Email Print
cover

Proceedings Paper

Design and implementation of a parallel object-oriented image processing toolkit
Author(s): Chandrika Kamath; Chuck H. Baldwin; Imola K. Fodor; Nu Ai Tang
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Advances in technology have enabled us to collect data from observations, experiments, and simulations at an ever increasing pace. As these data sets approach the terabyte and petabyte range, scientists are increasingly using semi-automated techniques from data mining and pattern recognition to find useful information in the data. In order for data mining to be successful, the raw data must first be processed into a form suitable for the detection of patterns. When the data is in the form of images, this can involve a substantial amount of processing on very large data sets. To help make this task more efficient, we are designing and implementing an object-oriented image processing toolkit that specifically targets massively-parallel, distributed-memory architectures. We first show that it is possible to use object-oriented technology to effectively address the diverse needs of image applications. Next, we describe how we abstract out the similarities in image processing algorithms to enable re-use in our software. We will also discuss the difficulties encountered in parallelizing image algorithms on the massively parallel machines as well as the bottlenecks to high performance. We will demonstrate our work using images from an astronomical data set, and illustrate how techniques such as filters and denoising through the thresholding of wavelet coefficients can be applied when a large image is distributed across several processors.

Paper Details

Date Published: 9 October 2000
PDF: 12 pages
Proc. SPIE 4118, Parallel and Distributed Methods for Image Processing IV, (9 October 2000); doi: 10.1117/12.403590
Show Author Affiliations
Chandrika Kamath, Lawrence Livermore National Lab. (United States)
Chuck H. Baldwin, Lawrence Livermore National Lab. (United States)
Imola K. Fodor, Lawrence Livermore National Lab. (United States)
Nu Ai Tang, Lawrence Livermore National Lab. (United States)


Published in SPIE Proceedings Vol. 4118:
Parallel and Distributed Methods for Image Processing IV
Hongchi Shi; Patrick C. Coffield; Divyendu Sinha, Editor(s)

© SPIE. Terms of Use
Back to Top