PT Journal
AU Sergio Escalera
   Ana Puig
   Oscar Amoros
   Maria Salamo
TI Intelligent GPGPU Classification in Volume Visualization: a framework based on Error-Correcting Output Codes
SO Computer Graphics Forum
JI CGF
PY 2011
BP 2107
EP 2115
VL 30
IS 7
DI 10.1111/j.1467-8659.2011.02043.x
AB IF JCR 1.455 2010 25/99In 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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