Journal of Computer Science & Systems Biology

Segmentation of Experimental Curves Distorted by Noise

Abstract

Vladimir Kalmykov and Anton Sharypanov

A new segmentation method of signals distorted by noise is proposed. Unlike other known methods, for example, the Canny method, a priori data on interference and/or a signal (image) is not used. Segmentation of signals and halftone images distorted by interference is one of the oldest problems in computer vision. But human vision solves this task almost independently of our consciousness. It was discovered for vision neurons, that sizes of receptive fields’ excitatory zones change during visual act, which eventually mean dynamical changes in visual system’s resolution i.e., coarse-to-fine phenomenon in living organism. We assumed that “coarse-to-fine” phenomenon, i.e., several different resolutions, is used in human vision to segment images. A “coarse-to-fine” algorithm for segmentation of experimental graphs was developed. The main difference of algorithm mentioned above from others is that decision is made taking into the account all partial solutions for all resolutions being used. This ensures stability of final global solution. The algorithm verification results are presented. It is expected that the method can naturally be expanded to segmentation of halftone images.