  This work presents a new procedure to extract features of grey-level texture
images based on the discrete Schroedinger transform. This is a non-linear
transform where the image is mapped as the initial probability distribution of
a wave function and such distribution evolves in time following the
Schroedinger equation from Quantum Mechanics. The features are provided by
statistical moments of the distribution measured at different times. The
proposed method is applied to the classification of three databases of textures
used for benchmark and compared to other well-known texture descriptors in the
literature, such as textons, local binary patterns, multifractals, among
others. All of them are outperformed by the proposed method in terms of
percentage of images correctly classified. The proposal is also applied to the
identification of plant species using scanned images of leaves and again it
outperforms other texture methods. A test with images affected by Gaussian and
"salt \& pepper" noise is also carried out, also with the best performance
achieved by the Schroedinger descriptors.
