Neuro-fuzzy reasoning for occluded object recognition

Abstract

To tackle the problem of occluded object recognition first we give a new interpretation to the multidimensional fuzzy reasoning and then realize that new interpretation through backpropagation-type neural network. At the learning stage of the neural network, fuzzy linguistic statements are used. Once learned, the nonfuzzy features of an occluded object can be classified. At the time of classification of the nonfuzzy features of an occluded object we use the concept of fuzzy singleton. An effective approach to recognize an unknown scene which consists of a set of occluded objects is to detect a number of significant (local) features on the boundary of the unknown scene. Thus the major problems fall into the selection of the appropriate set of features (local) for representing the object in the training stage, as well as in the detection of these features in the recognition process. The features should be invariant to scale, orientation and minor distortions in boundary shape. The performance of the proposed scheme is tested through several examples.