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Application of tensor approximation to multiscale volume feature representations


Suter, S K; Zollikofer, C P E; Pajarola, R (2010). Application of tensor approximation to multiscale volume feature representations. In: VMV 2010 - 15th International Workshop on Vision, Modeling and Visualization, Siegen, 15 November 2010 - 17 November 2010, 203-210.

Abstract

Advanced 3D microstructural analysis in natural sciences and engineering depends ever more on modern data acquisition and imaging technologies such as micro-computed or synchrotron tomography and interactive visualization. The acquired volume data sets are not only of high-resolution but in particular exhibit complex spatial structures at different levels of scale (e.g. variable spatial expression of multiscale periodic growth structures in tooth enamel). Such highly structured volume data sets represent a tough challenge to be analyzed and explored by means of interactive visualization due to the amount of raw volume data to be processed and filtered for the desired features. As an approach to address this bottleneck by multiscale feature preserving data reduction, we propose higher-order tensor approximations (TAs). We demonstrate the power of TA to represent, and highlight the structural features in volume data. We visually and quantitatively show that TA yields high data reduction and that TA preserves volume features at multiple scales.

Abstract

Advanced 3D microstructural analysis in natural sciences and engineering depends ever more on modern data acquisition and imaging technologies such as micro-computed or synchrotron tomography and interactive visualization. The acquired volume data sets are not only of high-resolution but in particular exhibit complex spatial structures at different levels of scale (e.g. variable spatial expression of multiscale periodic growth structures in tooth enamel). Such highly structured volume data sets represent a tough challenge to be analyzed and explored by means of interactive visualization due to the amount of raw volume data to be processed and filtered for the desired features. As an approach to address this bottleneck by multiscale feature preserving data reduction, we propose higher-order tensor approximations (TAs). We demonstrate the power of TA to represent, and highlight the structural features in volume data. We visually and quantitatively show that TA yields high data reduction and that TA preserves volume features at multiple scales.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
07 Faculty of Science > Department of Anthropology
Dewey Decimal Classification:300 Social sciences, sociology & anthropology
000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Computer Vision and Pattern Recognition
Physical Sciences > Modeling and Simulation
Uncontrolled Keywords:visualization, volume rendering, tensor approximation, feature detection
Language:English
Event End Date:17 November 2010
Deposited On:18 Feb 2011 22:01
Last Modified:06 May 2020 21:43
Publisher:Eurographics Association
ISBN:978-3-905673-79-1
Additional Information:Title of the book: VMV 2010 : Vision, Modeling & Visualization ; Siegen, November 15 - 17, 2010
OA Status:Green
Publisher DOI:https://doi.org/10.2312/PE/VMV/VMV10/275-282
Related URLs:http://vmv2010.uni-siegen.de/ (Organisation)

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