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TAMRESH – Tensor Approximation Multiresolution Hierarchy for Interactive Volume Visualization
Author(s) -
Suter S.K.,
Makhynia M.,
Pajarola R.
Publication year - 2013
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12102
Subject(s) - visualization , computer science , multiresolution analysis , volume rendering , rendering (computer graphics) , representation (politics) , hierarchy , data visualization , volume (thermodynamics) , interactive visualization , scientific visualization , feature (linguistics) , artificial intelligence , theoretical computer science , data mining , pattern recognition (psychology) , algorithm , wavelet , discrete wavelet transform , linguistics , philosophy , physics , wavelet transform , quantum mechanics , politics , political science , economics , law , market economy
Interactive visual analysis of large and complex volume datasets is an ongoing and challenging problem. We tackle this challenge in the context of state‐of‐the‐art out‐of‐core multiresolution volume rendering by introducing a novel hierarchical tensor approximation (TA) volume visualization approach. The TA framework allows us (a) to use a rank‐truncated basis for compact volume representation, (b) to visualize features at multiple scales, and (c) to visualize the data at multiple resolutions. In this paper, we exploit the special properties of the TA factor matrix bases and define a novel multiscale and multiresolution volume rendering hierarchy. Different from previous approaches, to represent one volume dataset we use but one set of global bases (TA factor matrices) to reconstruct at all resolution levels and feature scales. In particular, we propose a coupling of multiscalable feature visualization and multiresolution DVR through the properties of global TA bases. We demonstrate our novel TA multiresolution hierarchy based volume representation and visualization on a number of μCT volume datasets.

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