Simulation and weights of multiple cues for robust object recognition

Reliable recognition of objects is an important capability in order to have agents accomplish and assist in a variety of useful tasks such as search and rescue or office assistance. Numerous approaches attempt to recognize objects based on visual cues alone. However, the same type of object can have very different visual appearances, such as shape, size, pose, color. Although such approaches are widely studied with relative success, the general task of object recognition still remains difficult. In previous work, we introduced MCOR (Multiple-Cue Object Recognition), a flexible object recognition approach which can use any multiple cues, whether they are visual cues intrinsic to the object or provided by observation of a human. As part of the framework, weights were provided to reflect the variation in the strength of the association between a particular cue and an object. In this paper, we demonstrate how the probabilistic relational framework used to determine the weights can be used in complex scenarios with numerous objects, cues, and the relationship between them. We develop a simulator that can generate these complex scenarios using cues based on real recognition systems.