6301

Adaboost GPU-based Classifier for Direct Volume Rendering

Oscar Amoros, Sergio Escalera, Anna Puig
Barcelona Supercomputing Center – CNS, K2M Building, c/ Jordi Girona, 29 08034 Barcelona, Spain
International Conference on Computer Graphics Theory and Applications, 2011

@article{amoros2011adaboost,

   title={ADABOOST GPU-BASED CLASSIFIER FOR DIRECT VOLUME RENDERING},

   author={Amoros, O. and Escalera, S. and Puig, A.},

   year={2011}

}

Download Download (PDF)   View View   Source Source   

1081

views

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.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: