@Article{SergioEscalera2011,
author="Sergio Escalera
and Ana Puig
and Oscar Amoros
and Maria Salamo",
title="Intelligent GPGPU Classification in Volume Visualization: a framework based on Error-Correcting Output Codes",
journal="Computer Graphics Forum",
year="2011",
volume="30",
number="7",
pages="2107--2115",
abstract="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.",
optnote="MILAB; HuPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1881), last updated on Fri, 19 Sep 2014 10:31:13 +0200",
doi="10.1111/j.1467-8659.2011.02043.x"
}