A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf
.
Lacunarity Analysis on Image Patterns for Texture Classification
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
Based on the concept of lacunarity in fractal geometry, we developed a statistical approach to texture description, which yields highly discriminative feature with strong robustness to a wide range of transformations, including photometric changes and geometric changes. The texture feature is constructed by concatenating the lacunarity-related parameters estimated from the multi-scale local binary patterns of image. Benefiting from the ability of lacunarity analysis to distinguish spatial
doi:10.1109/cvpr.2014.28
dblp:conf/cvpr/QuanXSL14
fatcat:un266mnvmndixjewe2p7pkycm4