Tsallis Entropy for Geometry Simplification

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)

Tsallis Entropy for Geometry Simplification

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 surface simplification 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)This work was supported by the Spanish Ministry of Science and Innovation (Project TIN2010-21089-C03-03 and TIN2010-21089-C03-01) and Feder Funds, Bancaixa (Project P1.1B2010-08), Generalitat Valenciana (Project PROMETEO/2010/028) and Project 2009-SGR-643 of Generalitat de Catalunya (Catalan Government