Computational theories of object recognition.

This paper examines four current theoretical approaches to the representation and recognition of visual objects: structural descriptions, geometric constraints, multidimensional feature spaces and shape-space approximation. The strengths and weaknesses of the four theories are considered, with a special focus on their approach to categorization - a computationally challenging task which is not widely addressed in computer vision, where the stress is rather on the generalization of recognition across changes of viewpoint.