Contour completion algorithm quantitavely and qualitatively improves the performance of a recognition model (evidence against geon theory)

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, 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, b) increased the similarity of complementary feature-deleted images, but not part-deleted ones, more closely corresponding to human psychophysical results. The results demonstrate that incorporating the functional equivalent of the Gestalt principles of good continuation, co-curvilinearity and proximity (in the form of extension fields) to a biologically inspired recognition model can largely account for psychophysical data on basic-level human object recognition. This also indicates that the recovery of part structure (geons) might not be a necessary step for recognizing objects.