Recognition of partially occluded objects based on ARG model

The recognition of partially occluded objects is significant in computer vision.In this paper,a new algorithm of recognition of partially occluded objects based on ARG model is proposed.The ARG is robust to shape variations due to noise and occlusions and 2-D geometric transformation as well,so the objects with lost features caused by partially occluded or other reasons can be recognized by using the ARG model.The algorithm consists of two-phases.First,a finite number of candidate subgraphs are selected in an image,by using the logical constraint embedding local and structure consistency as well as the correspondence measure between model and image features.Second,the feature loss detection is done iteratively by the error detection and voting scheme through the error analysis in the relation vector space.Partial matching is performed after all lost features are detected.Finally,the effectiveness of the algorithm is demonstrated by the experiment.