In this paper

we propose a new approach to recognize occluded objects. The information of the magnitude of the local extreme of the open angles in the contour of a model(occluded image) at a scale

the information of the distance and relative location between the two adjacent dominant points are suitably integrated as a set of features for describing a model(occluded image)

the features are invariant under rotation

uniform scaling

and translation of the curve. The magnitude of opened angle at a pointpiin the contour can be easily calculated by the law of cosines

and its local extreme correspond to the sharper changes of the contour of the mode (scene). The feature matching is to define the correspondence between the model features and the scene features. Each correspondence between a model feature and a scene feature constitutes a“feature correspondence pair”

they are mapped onto the Hopfield neural network that is used to perform global feature matching. The proposed approach has been implemented on PⅡpersonal computer in Matlab5.2 programming language and examples are presented. The experimental results show that our proposed method can efficiently recognize an object from an image of occluded objects

and be implemented easily.