Efficient Local Histogram Searching via Bitmap Indexing

dc.contributor.authorWei, Tzu-Hsuanen_US
dc.contributor.authorChen, Chun-Mingen_US
dc.contributor.authorBiswas, Ayanen_US
dc.contributor.editorH. Carr, K.-L. Ma, and G. Santuccien_US
dc.date.accessioned2015-05-22T12:50:56Z
dc.date.available2015-05-22T12:50:56Z
dc.date.issued2015en_US
dc.description.abstractRepresenting features by local histograms is a proven technique in several volume analysis and visualization applications including feature tracking and transfer function design. The efficiency of these applications, however, is hampered by the high computational complexity of local histogram computation and matching. In this paper, we propose a novel algorithm to accelerate local histogram search by leveraging bitmap indexing. Our method avoids exhaustive searching of all voxels in the spatial domain by examining only the voxels whose values fall within the value range of user-defined local features and their neighborhood. Based on the idea that the value range of local features is in general much smaller than the dynamic range of the entire dataset, we propose a local voting scheme to construct the local histograms so that only a small number of voxels need to be examined. Experimental results show that our method can reduce much computational workload compared to the conventional approaches. To demonstrate the utility of our method, an interactive interface was developed to assist users in defining target features as local histograms and identify the locations of these features in the dataset.en_US
dc.description.number3en_US
dc.description.sectionheadersVolume Analysis and Classificationen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume34en_US
dc.identifier.doi10.1111/cgf.12620en_US
dc.identifier.pages081-090en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12620en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.titleEfficient Local Histogram Searching via Bitmap Indexingen_US
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