An object recognition system based on color co-occurrence histogram and geometric relations of pyramidal image patches

A service robot has to identify objects for given tasks by using the camera mounted on its body. In general, previously proposed object-recognition systems do not show enough performance to apply them to service robots. Since, the object-recognition system for service robots must be robust for environmental conditions and fast enough to support its tasks. Changes of Illumination, viewpoint and scale level are examples for those changes. In addition, the system must be robust for partially occluded cases. To satisfy those requirements, we propose the system with novel modeling and matching methods. Moreover, our system can show better performance by adjusting parameter based on environmental conditions. Through lots of tests for different environmental changes, our system is shown to be partially invariant to illumination and viewpoints. Moreover, our system verifies its robustness for various scale levels and partially occluded cases. Consequently, service robots can identify objects efficiently in environmental changes by adopting the recognition system we proposed.