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Parallel Algorithms for Image Histogramming and Connected Components with an Experimental Study

dc.contributor.authorBader, David A.en_US
dc.contributor.authorJaJa, Josephen_US
dc.description.abstractThis paper presents efficient and portable implementations of two useful primitives in image processing algorithms, histogramming and connected components. Our general framework is a single-address space, distributed memory programming model. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. Our connected components algorithm uses a novel approach for parallel merging which performs drastically limited updating during iterative steps, and concludes with a total consistency update at the final step. The algorithms have been coded in Split-C and run on a variety of platforms. Our experimental results are consistent with the theoretical analysis and provide the best known execution times for these two primitives, even when compared with machine specific implementations. More efficient implementations of Split-C will likely result in even faster execution times. (Also cross-referenced as UMIACS-TR-94-133.)en_US
dc.format.extent3257675 bytes
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-3384en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-94-133.en_US
dc.titleParallel Algorithms for Image Histogramming and Connected Components with an Experimental Studyen_US
dc.typeTechnical Reporten_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US

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