A statistical correlation technique and a neural network for the motion correspondence problem

Abstract

A statistical correlation technique (SCT) and two variants of a neural network are presented to solve the motion correspondence problem. Solutions of the motion correspondence problem aim to maintain the identities of individuated elements as they move. In a pre-processing stage, two snapshots of a moving scene are convoluted with two-dimensional Gabor functions, which yields orientations and spatial frequencies of the snapshots at every position. In this paper these properties are used to extract, respectively, the attributes orientation, size and position of line segments. The SCT uses cross-correlations to find the correct translation components, angle of rotation and scaling factor. These parameters are then used in combination with the positions of the line segments to calculate the centre of motion. When all of these parameters are known, the new positions of the line segments from the first snapshot can be calculated and compared to the features in the second snapshot. This yields the solution of the motion correspondence problem. Since the SCT is an indirect way of solving the problem, the principles of the technique are implemented in interactive activation and competition neural networks. With boundary problems and noise these networks perform better than the SCT. They also have the advantage that at every stage of the calculations the best candidates for corresponding pairs of line segments are known.