Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6856275 | Information Sciences | 2018 | 17 Pages |
Abstract
This work proposes a method to extract features from texture images by applying a Gaussian pyramid multiscale approach to the Bouligand-Minkowski fractal descriptors. The proposal starts from the texture image and computes the stack of multi-resolution images that compose the pyramid, in both directions, of reduction and expansion. In the following, each image in the stack is mapped onto a surface, which is dilated by spheres with variable radii and the dilation volumes are used to compute the Bouligand-Minkowski fractal descriptors for each level. Both the descriptors of each level and combinations with descriptors from the original image are verified in the classification of well-known databases of textural images. The proposed method outperformed other classical and state-of-the-art descriptors with a significant advantage in most cases, including situations where random noise is added to the images.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
João Batista Florindo, Dalcimar Casanova, Odemir Martinez Bruno,