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Visibility-difference entropy for automatic transfer function generation


Schlegel, Philipp; Pajarola, R (2013). Visibility-difference entropy for automatic transfer function generation. In: Proceedings SPIE Conference on Visualization and Data Analysis, San Francisco, 3 February 2013 - 7 February 2013, 865406.

Abstract

Direct volume rendering allows for interactive exploration of volumetric data and has become an important tool in many visualization domains. But the insight and information that can be obtained are dependent on the transfer function defining the transparency of voxels. Constructing good transfer functions is one of the most time consuming and cumbersome tasks in volume visualization. We present a novel general purpose method for automatically generating an initial set of best transfer function candidates. The generated transfer functions reveal the major structural features within the volume and allow for an efficient initial visual analysis, serving as a basis for further interactive exploration in particular of originally unknown data. The basic idea is to introduce a metric as a measure of the goodness of a transfer function which indicates the information that can be gained from rendered images by interactive visualization. In contrast to prior methods, our approach does not require a user feedback-loop, operates exclusively in image space and takes the characteristics of interactive data exploration into account. We show how our new transfer function generation method can uncover the major structures of an unknown dataset within only a few minutes.

Abstract

Direct volume rendering allows for interactive exploration of volumetric data and has become an important tool in many visualization domains. But the insight and information that can be obtained are dependent on the transfer function defining the transparency of voxels. Constructing good transfer functions is one of the most time consuming and cumbersome tasks in volume visualization. We present a novel general purpose method for automatically generating an initial set of best transfer function candidates. The generated transfer functions reveal the major structural features within the volume and allow for an efficient initial visual analysis, serving as a basis for further interactive exploration in particular of originally unknown data. The basic idea is to introduce a metric as a measure of the goodness of a transfer function which indicates the information that can be gained from rendered images by interactive visualization. In contrast to prior methods, our approach does not require a user feedback-loop, operates exclusively in image space and takes the characteristics of interactive data exploration into account. We show how our new transfer function generation method can uncover the major structures of an unknown dataset within only a few minutes.

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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
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:7 February 2013
Deposited On:16 Oct 2014 11:29
Last Modified:16 Feb 2018 20:00
Publisher:SPIE - International Society for Optical Engineering
Series Name:Proceedings of SPIE
ISSN:0277-786X
OA Status:Green
Publisher DOI:https://doi.org/10.1117/12.2002971
Other Identification Number:merlin-id:8898

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