This paper describes results from an ongoing project concerned with recognizing objects in complex scene domains, especially in the domain that includes the natural outdoor world. Traditional machine recognition paradigms assume either 1) that all objects of interest are definable by a relatively small number of explicit shape models or 2) that all objects of interest have characteristic, locally measurable features. The failure of both assumptions in a complex domain such as the natural outdoor world has a dramatic impact on the form of an acceptable architecture for an object recognition system. In our work, we make the use of contextual information a central issue, and explicitly design a system to identify and use context as an integral part of recognition. In so doing, we provide a new paradigm for visual recognition that eliminates the traditional dependence on stored geometric models and universal image partitioning algorithms. This paradigm combines the results of many simple procedures that analyze monochrome, color, stereo, or 3-D range images. By interpreting their results along with relevant contextual knowledge, a reliable recognition result is achieved, even in the face of imperfect visual procedures. Initial experimentation with the system on ground-level outdoor imagery has already demonstrated competence beyond what we believe is attainable with other existing vision systems.
