  Computer vision is a growing field with a lot of new applications in
automation and robotics, since it allows the analysis of images and shapes for
the generation of numerical or analytical information. One of the most used
method of information extraction is image filtering through convolution
kernels, with each kernel specialized for specific applications. The objective
of this paper is to present a novel convolution kernels, based on principles of
electromagnetic potentials and fields, for a general use in computer vision and
to demonstrate its usage for shape and stroke analysis. Such filtering
possesses unique geometrical properties that can be interpreted using well
understood physics theorems. Therefore, this paper focuses on the development
of the electromagnetic kernels and on their application on images for shape and
stroke analysis. It also presents several interesting features of
electromagnetic kernels, such as resolution, size and orientation independence,
robustness to noise and deformation, long distance stroke interaction and
ability to work with 3D images
