The Radon Signed Cumulative Distribution Transform and its applications
  in classification of Signed Images
Le Gong Shiying Li Naqib Sad Pathan Mohammad Shifat-E-Rabbi Gustavo K. Rohde Abu Hasnat Mohammad Rubaiyat Sumati Thareja
Here we describe a new image representation technique based on the
mathematics of transport and optimal transport. The method relies on the
combination of the well-known Radon transform for images and a recent signal
representation method called the Signed Cumulative Distribution Transform. The
newly proposed method generalizes previous transport-related image
representation methods to arbitrary functions (images), and thus can be used in
more applications. We describe the new transform, and some of its mathematical
properties and demonstrate its ability to partition image classes with real and
simulated data. In comparison to existing transport transform methods, as well
as deep learning-based classification methods, the new transform more
accurately represents the information content of signed images, and thus can be
used to obtain higher classification accuracies. The implementation of the
proposed method in Python language is integrated as a part of the software
package PyTransKit, available on Github.