@InProceedings{OscarAmoros2011,
author="Oscar Amoros
and Sergio Escalera
and Anna Puig",
title="Adaboost GPU-based Classifier for Direct Volume Rendering",
booktitle="International Conference on Computer Graphics Theory and Applications",
year="2011",
pages="215--219",
abstract="In volume visualization, the voxel visibitity and materials are carried out through an interactive editing of Transfer Function. In this paper, we present a two-level GPU-based labeling method that computes in times of rendering a set of labeled structures using the Adaboost machine learning classifier. In a pre-processing step, Adaboost trains a binary classifier from a pre-labeled dataset and, in each sample, takes into account a set of features. This binary classifier is a weighted combination of weak classifiers, which can be expressed as simple decision functions estimated on a single feature values. Then, at the testing stage, each weak classifier is independently applied on the features of a set of unlabeled samples. We propose an alternative representation of these classifiers that allow a GPU-based parallelizated testing stage embedded into the visualization pipeline. The empirical results confirm the OpenCL-based classification of biomedical datasets as a tough problem where an opportunity for further research emerges.",
optnote="MILAB; HuPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1774), last updated on Fri, 19 Sep 2014 10:28:42 +0200"
}