  This work proposes and study the concept of Functional Data Analysis
transform, applying it to the performance improving of volumetric
Bouligand-Minkowski fractal descriptors. The proposed transform consists
essentially in changing the descriptors originally defined in the space of the
calculus of fractal dimension into the space of coefficients used in the
functional data representation of these descriptors. The transformed decriptors
are used here in texture classification problems. The enhancement provided by
the FDA transform is measured by comparing the transformed to the original
descriptors in terms of the correctness rate in the classification of well
known datasets.
