Recognizing partially occluded objects using Markov model
A novel method to recognize occluded objects using Markov model is proposed in this paper. In addition to Markov model having a high tolerance to noise, spatial distribution of features can be incorporated into Markov model in a natural and elegant way. Thus, high recognition accuracy can be achieved by the proposed method. More specifically, for each occluded object in the scene image, its translation, rotation and scale parameters can all be determined by our method even when it may have transformation parameters different from others or it may be duplicated in the scene image with transformation parameters different from each other. Moreover, the recognition process can be performed step by step to find out all of the objects in the scene image according to the confidence measure. Finally, the recognition process can be terminated automatically without knowing the number of objects included in the scene image since hypothesis verification and termination test are performed in our method. Actually, the solution of our method is useful for depth-search applications such as inspection of printed circuit board with multiple layers, underwater diving for searching objects, underground drilling for exploring mine, etc. The proposed method has been applied on two types of databases: puzzle and tool. The effectiveness and practicability of the proposed approach have been proven by various experimental results.
occluded objects markov model spatial distribution of features hypothesis verification object recognition termination criteria