TY - JOUR
T1 - Intelligent GPGPU classification in volume visualization
T2 - A framework based on error-correcting output codes
AU - Escalera, S.
AU - Puig, A.
AU - Amoros, O.
AU - Salamó, M.
N1 - Publisher Copyright:
© 2011 The Author(s).
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84881373723&partnerID=8YFLogxK
U2 - 10.1111/j.1467-8659.2011.02043.x
DO - 10.1111/j.1467-8659.2011.02043.x
M3 - Journal article
AN - SCOPUS:84881373723
SN - 0167-7055
VL - 30
SP - 2107
EP - 2115
JO - Computer Graphics Forum
JF - Computer Graphics Forum
IS - 7
ER -