Tsallis Entropy for Geometry Simplification

Entropy

Pascual Castelló¹  Carlos González¹  Miguel Chover¹  Mateu Sbert¹ 

[1] ¹Departamento de Lenguajes y Sistemas Informáticos, Institute of New Imaging Technologies, Universitat Jaume I, Campus de Riu Sec, Castellón E-12071, Spain ²Institut d’Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, Girona E-17071, Spain

关键词: information theory;    viewpoint information measures;    mesh simplification;   

DOI  :  10.3390/e13101805

来源: mdpi

【 摘 要 】

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).

【 授权许可】

CC BY   
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.