Consistent Visual Information Processing Applied to Object Recognition Landmark Definition and Real-Time Tracking

The handling of situations where multiple visual information occurs requires the fusion of visual information. This is a very common task found in the processing of multisource / multitemporal datasets, in sensor fusion, and in all kinds of active vision systems. A general approach to this problem is presented which goes beyond previous information theoretic investigations. Starting from the paradigm of ‘Active Fusion’, where entropy is used as a measure to evaluate the expected gain in information from a potential data source, we develop the concept of data ‘consistency’. In multisource visual information processing, consistency can be expressed by vicinity in space, by similarity of visual landmarks or by higher level constraints like smoothness of motion trajectories, rigid body, or continuity constraints. Several sample applications are presented, including an active object recognition system, the definition of salient landmarks, and an optical tracking system. In summary, consistency evaluation is a powerful method to reduce complexity and to resolve otherwise ill-posed problems like ambiguity in computer vision.