OCCLUDED OBJECTS RECOGNITION USING MULTISCALE FEATURES AND HOPFIELD NEURAL-NETWORK

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 sta te, the matching results heavily depend on the initial network state d etermined by the extracted features. To acquire more satisfactory init ial matching results, a new feature vector was developed which consist s of the multiscale evolution of the extremal position and magnitude o f the wavelet transformed contour orientation. These features can even be used to discriminate dominant points, hence good initial states ca n be obtained. The good initiation enables our proposed method to reco gnize objects even heavily occluded, that cannot be achieved by using the Nasrabadi-Li's method. In addition, to make the matching results m ore insensitive to the threshold value selection of the network, we re place the step-like thresholding function by a ramp-like one. Experime ntal results have shown that our method is effective even for noisy oc cluded objects. Copyright (C) 1996 Pattern Recognition Society.