TY  - CONF
AU  - Oscar Amoros
AU  - Sergio Escalera
AU  - Anna Puig
A2  - GRAPP
PY  - 2011//
TI  - Adaboost GPU-based Classifier for Direct Volume Rendering
BT  - International Conference on Computer Graphics Theory and Applications
SP  - 215
EP  - 219
N2  - 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.
N1  - MILAB; HuPBA
ID  - Oscar Amoros2011
ER  -