Self-Correcting Neural Network for Stereo-matching Problem Solving

Abstract

In the present paper we describe innovative architecture of artificial neural network based on Hopfield structure Self Correcting Neural Network (SCNN). It is implementation similar to dual mode Hopfield-like network for solving stereo matching problem. Considered network consists of basic layer of neurons implemented as analogue Hopfield-like network and supervising layer. Thanks to the supervising layer, there is a possibility of modification of the connection weights between the neurons in the basic layer. This enables the improvement of the network performance (accuracy). Authors propose a depth map use for image segmentation and objects auto-selection. High enough accuracy of these processes can be achieved when proposed network (SCNN) is applied. Similar idea can be applied also for images noise removal. In the present article we also describe in detail neurons dynamics in the basic and supervising layers of the SCNN. The network considered here was a subject of experimental tests using real stereo pictures as well as simulated stereo images. This enabled calculation of error and direct comparison with classic analogue Hopfield neural network.