Object recognition of robot based on hidden Markov models

We propose a new scheme for object recognition in robot vision. The proposed scheme uses the HSI model as the input. A gradient algorithm is used to obtain the edge estimation of objects. The edge points are regarded as stimulus and the other points as nonstimulus. The stimulus context of all the possible positions of objects is coded in stimulus vectors. Then a HMM-based vision system for scene analyse and object recognition is presented. This method has been tested using our mobile service robot called ARNOLD and real data of Columbia Object Image Library (COIL-20). Through these experiments, we have demonstrated the generalisation capabilities and robustness of object recognition and classification.