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

S. Escalera, A. Puig, O. Amoros, M. Salamo
Dept. Matematica Aplicada i Analisi, Universitat de Barcelona, Spain
Computer Graphics Forum, Volume 30, Issue 7, pages 2107-2115, 2011

@inproceedings{escalera2011intelligent,

   title={Intelligent GPGPU Classification in Volume Visualization: A framework based on Error-Correcting Output Codes},

   author={Escalera, S. and Puig, A. and Amoros, O. and Salam{‘o}, M.},

   year={2011},

   booktitle={Computer Graphics Forum},

   volume={30},

   number={7},

   pages={2107–2115},

   organization={Wiley Online Library}

}

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In 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.
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