Intelligent GPGPU Classification in Volume Visualization: A framework based on Error-Correcting Output Codes
| dc.contributor.author | Escalera, Sergio | en_US |
| dc.contributor.author | Puig, Anna | en_US |
| dc.contributor.author | Amoros, Oscar | en_US |
| dc.contributor.author | Salamó, Maria | en_US |
| dc.contributor.editor | Bing-Yu Chen, Jan Kautz, Tong-Yee Lee, and Ming C. Lin | en_US |
| dc.date.accessioned | 2015-02-27T16:14:13Z | |
| dc.date.available | 2015-02-27T16:14:13Z | |
| dc.date.issued | 2011 | en_US |
| dc.description.abstract | In volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on-demand multiple regions of interest. These methods can be split into a two-level GPU-based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error-Correcting Output Codes (ECOC) framework. In a pre-processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre-labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi-class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU-OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy. | en_US |
| dc.description.seriesinformation | Computer Graphics Forum | en_US |
| dc.identifier.doi | 10.1111/j.1467-8659.2011.02043.x | en_US |
| dc.identifier.issn | 1467-8659 | en_US |
| dc.identifier.uri | https://doi.org/10.1111/j.1467-8659.2011.02043.x | en_US |
| dc.publisher | The Eurographics Association and Blackwell Publishing Ltd. | en_US |
| dc.title | Intelligent GPGPU Classification in Volume Visualization: A framework based on Error-Correcting Output Codes | en_US |