Modeling human visual object recognition

The topics discussed here are network models of object recognition; a computational theory of recognition; psychophysical support for a view-interpolation model: and an open issue, features of recognition. The authors survey a successful replication of central characteristics of performance in 3-D object recognition by a computational model based on interpolation among a number of stored views of each object. Network models of 3-D object recognition based on interpolation among specific stored views behave in several respects similarly to human observers in a number of recognition tasks. Even closer replication of human performance in recognition should be expected, once the issue of the features used to represent object views is resolved.<<ETX>>

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