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Entropy 2011, 13(10), 1805-1828; doi:10.3390/e13101805
Article

Tsallis Entropy for Geometry Simplification

1,* , 1, 1, 2 and 2
1 Departamento de Lenguajes y Sistemas Informáticos, Institute of New Imaging Technologies, Universitat Jaume I, Campus de Riu Sec, Castellón E-12071, Spain 2 Institut d’Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, Girona E-17071, Spain
* Author to whom correspondence should be addressed.
Received: 1 August 2011 / Revised: 20 September 2011 / Accepted: 27 September 2011 / Published: 29 September 2011
(This article belongs to the Special Issue Tsallis Entropy)
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Abstract

This paper presents a study and a comparison of the use of different information-theoretic measures for polygonal mesh simplification. Generalized measures from Information Theory such as Havrda–Charvát–Tsallis entropy and mutual information have been applied. These measures have been used in the error metric of a surfaces implification algorithm. We demonstrate that these measures are useful for simplifying three-dimensional polygonal meshes. We have also compared these metrics with the error metrics used in a geometry-based method and in an image-driven method. Quantitative results are presented in the comparison using the root-mean-square error (RMSE).
Keywords: information theory; viewpoint information measures; mesh simplification information theory; viewpoint information measures; mesh simplification
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Castelló, P.; González, C.; Chover, M.; Sbert, M.; Feixas, M. Tsallis Entropy for Geometry Simplification. Entropy 2011, 13, 1805-1828.

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