Computational understanding and modeling of filling-in process at the blind spot

Abstract

A visual model for filling-in at the blind spot is proposed. The general scheme of standard regularization theory is used to derive a visual model deductively. First, we indicate problems of the diffusion equation, which is frequently used for various kinds of perceptual completion. Then, we investigate the computational meaning of a neural property discovered by Matsumoto and Komatsu (J. Neurophysiology, vol. 93, pp. 2374-2387, 2005) and introduce second derivative quantities related to image geometry into a priori knowledge of missing images on the blind spot. Moreover, two different information pathways for filling-in (slow conductive paths of horizontal connections in V1, and fast feedforward/feedback paths via V2) are regarded as the neural embodiment of adiabatic approximation between V1 and V2 interaction. Numerical simulations show that the outputs of the proposed model for filling-in are consistent with a neurophysiological experimental result, and that the model is a powerful tool for digital image inpainting. © 2008 Springer-Verlag Berlin Heidelberg.