Object recognition by a Hopfield neural network
A two-dimensional model-based object recognition technique is introduced to identify and locate isolated or overlapping 2-D objects in any position and orientation. A cooperative feature-matching technique is proposed that is implemented by a Hopfield neural network. The proposed matching technique uses the parallelism of the neural network to globally match all the objects in the input scene against all the object models in the model-database at the same time. A global model graph representing all the object models is constructed where each node in the graph represents a feature that has a numerical feature value and is connected to other nodes by an arc representing the relationship or compatibility between them. Object recognition is formulated as matching this global model with an input scene graph representing a single object or several overlapping objects. The performance of the proposed technique is compared with that of a relaxation technique. >