A stochastic neural model for fast classification of binary images

In this article, we propose a new approach for fast recognition of objects from two-dimensional binary images using descriptors of curvature, the moment and an artificial neural network. This model associates a coefficient of certainty for each classification. Two image descriptors where used, the Hu moments and curvature scale space, to provide a reduced representation invariant to image transformations, and a neural network applying a Gibbs distribution of probability is used to calculate the coefficient of certainty to link an image to one class. A benchmark data set is used to demonstrate the usefulness of the proposed methodology. The robustness of the proposed approach is also evaluated under rotation, scale transformations. The evaluation of the performance is based on the accuracy in the framework of a Monte Carlo experiment.