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



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



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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.
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