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Abstract:

This publication can be retrieved by anonymous ftp to publications.ai.mit.edu. Most model-based methods for object recognition require a detailed knowledge of the correspondence between model and image features. Correspondence, however, is a difficult problem in its own right. We suggest a model-based technique to establish image-to-model correspondence and, therefore, to facilitate the recognition of objects. Our correspondence approach uses the model to guide and constrain the matching process. We use the model to roughly align image and model features. We then derive an estimate of a sparse number of matching model and image contours. Finally, we constrain the rest of the matches via global information from the model. These stages can repeated to refine the resulting correspondence. We have incorporated our technique into the linear combination object recognition scheme and have tested the entire system successfully on a variety of objects. There are four benefits to our approach. First, it is computationally simple. Second it is efficient; the use of models constrains the matches in a linear fashion. Third, it is effective; experiments show that our procedure quickly converges to a solution, if one exists. Finally, the procedure is robust; errors in the rough alignment stage do not impair the subsequent correspondence procedure.

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