Improvement of image processing by using homogeneous neural networks with fractional derivatives theorem
Zbigniew Gomółka, Bogusław Twaróg, Jacek Bartman · AIMS Press eBooks · 2011
The present paper deals with the unique circumvention of designing feed forward neural networks in the task of the interferometry image recog-nition. In order to bring the interferometry techniques to the fore, we recallbriefly that this is one of the modern techniques of restitution of three di-mensional shapes of the observed object on the basis of two dimensional flatlike images registered by CCD camera. The preliminary stage of this processis conducted with ridges detection, and to solve this computational task thediscussed neural network was applied. By looking for the similarities in the biological neural systems authors show the designing process of the homogeneousneural network in the task of maximums detection. The fractional derivativetheorem has been involved to assume the weight distribution function as wellas transfer functions. To ensure reader that the theoretical considerations arecorrect, the comprehensive review of experiment results with obtained two dimensional signals have been presented too.