Intelligent GPGPU Classification in Volume Visualization: A framework based on Error-Correcting Output Codes

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dc.contributor.authorEscalera, Sergioen_US
dc.contributor.authorPuig, Annaen_US
dc.contributor.authorAmoros, Oscaren_US
dc.contributor.authorSalamó, Mariaen_US
dc.contributor.editorBing-Yu Chen, Jan Kautz, Tong-Yee Lee, and Ming C. Linen_US
dc.date.accessioned2015-02-27T16:14:13Z
dc.date.available2015-02-27T16:14:13Z
dc.date.issued2011en_US
dc.description.abstractIn volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on-demand multiple regions of interest. These methods can be split into a two-level GPU-based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error-Correcting Output Codes (ECOC) framework. In a pre-processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre-labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi-class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU-OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2011.02043.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleIntelligent GPGPU Classification in Volume Visualization: A framework based on Error-Correcting Output Codesen_US
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