Structural analysis of stereograms for CNN depth detection

Abstract

The usefulness of combining stereogram techniques with cellular neural network (CNN) analogic procedures in stereo depth extraction has been demonstrated before. Due to the local processing philosophy in the CNN paradigm, the detailed and well-established knowledge of local stereogram properties is of high importance. The paper gives an algorithmic definition for synthetic stereograms; a means for structural analysis of different types of random stereograms. In order to confine the generally propagating effect of three-dimensional (3-D) surface features into a restricted neighborhood in a stereogram, the concept of difference stereograms and the related difference surface is introduced for coding local surface variations into stereograms. The difference stereogram, with its local structure, is especially suitable for CNN processing, and it is closely connected to the pattern projection technique of depth extraction.

Applications of Cellular Neural Networks for Shape from Shading Problem

“…The most popular application for CNN has been in image processing, essentially because of their analog feature and sparse connections, which are conductive to realtime processing [1], [8], [9].…”