Although many image processing applications are ideally suited for parallel implementation, most researchers in imaging do not benefit from high performance computing on a daily basis. Essentially, this is due to the fact that no parallelization tools exist that truly match the image processing researcher�s frame of reference. As it is unrealistic to expect imaging researchers to become experts in parallel computing, tools must be provided to allow them to develop high performance applications in a highly familiar manner. In an attempt to provide such a tool, we have designed a software architecture that allows transparent (i.e., sequential) implementation of data parallel imaging applications for execution on homogeneous distributed memory MIMDstyle multicomputers. This paper gives an assessment of the architecture�s effectiveness in providing significant performance gains. In particular, we describe the implementation and automatic parallelization of three well-known example applications that contain many fundamental imaging operations: (1) template matching, (2) multi-baseline stereo vision, and (3) line detection. Based on experimental results we conclude that our architecture constitutes a powerful and user-friendly tool for obtaining high performance in many important image processing research areas.

@InProceedings{SeinstraPDCIP2002,
author = "Seinstra, F. J. and Koelma, D. C. and Geusebroek, J. M. and Verster, F. C.
and Smeulders, A. W. M.",
title = "Efficient Applications in User Transparent Parallel Image Processing",
booktitle = "IPDPS Workshop on Parallel and Distributed Computing in Image Processing",
year = "2002",
url = "https://ivi.fnwi.uva.nl/isis/publications/2002/SeinstraPDCIP2002",
pdf = "https://ivi.fnwi.uva.nl/isis/publications/2002/SeinstraPDCIP2002/SeinstraPDCIP2002.pdf",
has_image = 1
}