Recognition of occluded objects: A cluster-structure algorithm

Abstract

Clustering techniques have been used to perform image segmentation, to detect lines and curves in images and to solve several other problems in pattern recognition and image analysis. In this paper we apply clustering methods to a new problem domain and present a new method based on a cluster-structure approach for the recognition of 2-D partially occluded objects. Basically, the technique consists of three steps: clustering of border segment transformations; finding continuous sequences of segments in appropriately chosen clusters; and clustering of sequence average transformation values. As compared to some of the earlier methods, which identify an object based on only one sequence of matched segments, the new approach allows the identification of all parts of the model which match in the occluded scene. We also discuss the application of the clustering techniques to 3-D scene analysis. In both cases, the cluster-structure algorithm entails the application of clustering concepts in a hierarchical manner, resulting in a decrease in the computational effort as the recognition algorithm progresses. The implementation of the techniques discussed for the 2-D case has been completed and the algorithm has been evaluated with respect to a large number of examples where several objects partially occlude one another. The method is able to tolerate a moderate change in scale and a significant amount of shape distortion arising as a result of segmentation and/or the polygonal approximation of the boundary of the object. A summary of the results is presented.