The form in which these models are best stored depends on the kind of information available in the input, and on the trade-off between the amount of memory allocated for the storage and the degree of sophistication required of the recognition process. In computer vision, a distinction can be made between representation schemes that use 3D object-centered coordinate systems and schemes that store viewpoint-specific information such as 2D views of objects.

We demonstrate the ability of a two-layer network of thresholded summation units to support representation of 3D objects in which several distinct 2D views are stored for ea.ch object. Using unsupervised Hebbian relaxation, the network learned to recognize ten objects from different viewpoints. The training process led to the emergence of compact representations of the specific input views. When tested on novel views of the same objects, the network exhibited a substantial generalization capability. In simulated psychophysical experiments, the network's behavior was qualitatively similar to that of human subjects.

We demonstrate the ability of a two-layer network of thresholded summation units to support representation of 3D objects in which several distinct 2D views are stored for ea.ch object. Using unsupervised Hebbian relaxation, the network learned to recognize ten objects from different viewpoints. The training process led to the emergence of compact representations of the specific input views. When tested on novel views of the same objects, the network exhibited a substantial generalization capability. In simulated psychophysical experiments, the network's behavior was qualitatively similar to that of human subjects.

We demonstrate the ability of a two-layer network of thresholded summation units to support representation of 3D objects in which several distinct 2D views are stored for ea.ch object. Using unsupervised Hebbianrelaxation, the network learned to recognize ten objects from different viewpoints. The training process led to the emergence of compact representations of the specific input views. When tested on novel views of the same objects, the network exhibited asubstantial generalization capability. In simulated psychophysical experiments,the network's behavior was qualitatively similar to that of human subjects.