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Biologically inspired recognition model with extension fields

A recognition model which defines a measure of shape similarity on the direct output of multiscale and multiorientation Gabor filters does not manifest qualitative aspects of human object recognition of contour-deleted images in that: (a) it recognizes recoverable and nonrecoverable contour-deleted images equally well whereas humans recognize recoverable images much better, and (b) it distinguishes complementary feature-deleted images whereas humans do not. Adding some of the known connectivity pattern of the primary visual cortex to the model in the form of extension fields (connections between collinear and curvilinear units) among filters increased the overall recognition performance of the model and: (a) boosted the recognition rate of the recoverable images far more than the nonrecoverable ones, and (b) increased the similarity of complementary feature-deleted images, but not part-deleted ones, more closely corresponding to human psychophysical results. Interestingly, the performance was approximately equivalent for narrow (/spl plusmn/15/spl deg/) and broad (/spl plusmn/90/spl deg/) extension fields. The described method is most promising for the processing of noisy input images.