Tsallis Entropy for Geometry Simplification
AbstractThis 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).
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Castelló, P.; González, C.; Chover, M.; Sbert, M.; Feixas, M. Tsallis Entropy for Geometry Simplification. Entropy 2011, 13, 1805-1828.
Castelló P, González C, Chover M, Sbert M, Feixas M. Tsallis Entropy for Geometry Simplification. Entropy. 2011; 13(10):1805-1828.Chicago/Turabian Style
Castelló, Pascual; González, Carlos; Chover, Miguel; Sbert, Mateu; Feixas, Miquel. 2011. "Tsallis Entropy for Geometry Simplification." Entropy 13, no. 10: 1805-1828.