The Challenge of Generic Object Recognition

We discuss the issues and challenges in the development of generic object recognition systems. We argue that high-level, volumetric part-based, descriptions are essential if we want to recognize objects which are similar but not identical to pre-stored models, under wide viewing conditions, and to automatically learn new models and add them to our knowledge base.We discuss the representation scheme and its relationships to the description extraction, recognition and learning processes. We then describe the difficulties in obtaining such descriptions from images and outline steps for robust and efficient implementations. We also demonstrate the viability of the arguments by reporting on recent progress.

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