Occluded objects recognition using multiscale features and hopfield neural network

Abstract A new method to recognize partially visible two-dimensional objects by means of multiscale features and Hopfield neural network was proposed. The Hopfield network was employed to perform global feature matching. Since the network only guarantees to converge to a local optimal state, the matching results heavily depend on the initial network state determined by the extracted features. To acquire more satisfactory initial matching results, a new feature vector was developed which consists of the multiscale evolution of the extremal position and magnitude of the wavelet transformed contour orientation. These features can even be used to discriminate dominant points, hence good initial states can be obtained. The good initiation enables our proposed method to recognize objects even heavily occluded, that cannot be achieved by using the Nasrabadi-Li's method. In addition, to make the matching results more insensitive to the threshold value selection of the network, we replace the step-like thresholding function by a ramp-like one. Experimental results have shown that our method is effective even for noisy occluded objects.