%0 Conference Proceedings
%T Adaboost GPU-based Classifier for Direct Volume Rendering
%A Oscar Amoros
%A Sergio Escalera
%A Anna Puig
%B International Conference on Computer Graphics Theory and Applications
%D 2011
%F Oscar Amoros2011
%O MILAB; HuPBA
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=1774), last updated on Fri, 19 Sep 2014 10:28:42 +0200
%X 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.
%P 215-219