3D position sensing using a Hopfield neural network stereo matching algorithm

This paper presents a new algorithm for finding the point of correspondence in stereo images using a Hopfield neural network (HNN). A 2D HNN is used to match primitives extracted form one image to the other. The features used for this operation are edge points extracted using the Sobel operator. A formulation of the stereo correspondence as an optimization problem has been presented where various geometrical properties of multiple view geometry including disparity gradient (DG) have been used as constraints on the objective function. The 2D HNN is then used to minimize this function, thus achieving an optimum correspondence from one to the other image. The results show higher accuracy than template matching and local search methods