Neural-networks-based edges selector for boundary extraction problems

In the present work, a neural-networks-based system is presented that makes it possible to reduce, when generating edge maps to be used in an object boundary detection problem, the number of edges that are not due to the object itself, but to the background. Starting from a conventional edge detection, the selection is carried out by a neuralnetworks-based classifier, which is trained using examples. As a test for the system, the application to bovine livestock images is presented, from which we want to extract the boundary of the animal.