The appearance of an object in a complex scene can be influenced by a number of factors. One of the factors which most complicates the automated recognition of objects is occlusion.
Traditional techniques focus on recognizing single objects in an image.
Without explicit knowledge about an object's relationship to the scene, these missing portions can never be inferred as occluded. This dissertation focuses on recognizing objects in the context of a scene. In such a framework, partial scene models are constructed to account for occluded object surfaces in terms of other hypothesized objects in the scene. To facilitate such reasoning, standard graphical rendering techniques are employed to generate adequate predictions of an imaged scene. Recognition is then cast as a search for the parameters of a scene model which best aligns the prediction to the observed imagery.