IEEE Transactions on Parallel and Distributed Systems
User Transparent Parallel Processing of the 2004 NIST TRECVID Data Set
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
Commodity cluster-based parallel processing of hyperspectral imagery
Journal of Parallel and Distributed Computing
Parallel processing for image and video processing: Issues and challenges
Parallel Computing
High-performance SIMT code generation in an active visual effects library
Proceedings of the 6th ACM conference on Computing frontiers
A Grid framework to enable parallel and concurrent TMA image analyses
International Journal of Grid and Utility Computing
Parallel implementation of mobile robotic self-localization
Proceedings of the 2009 International Conference on Hybrid Information Technology
User transparent task parallel multimedia content analysis
Euro-Par'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part II
User transparent data and task parallel multimedia computing with Pyxis-DT
Future Generation Computer Systems
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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 MIMD-style multicomputers. This paper presents an extensive overview of the design rationale behind the software architecture, and 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 software architecture constitutes a powerful and user-friendly tool for obtaining high performance in many important image processing research areas. Copyright © 2004 John Wiley & Sons, Ltd.