Proposes a network mechanism for the determination of apparent tilt based on human psychophysics and physiology. The network model is capable of determining the apparent orientation of bars, including those in various types of tilt illusion. The model extracts local orientation information by VI-cell-like units with a Gabor-like receptive field. The activities of the units are grouped together based on collinear synchronization observed in a VI superficial layer. The model shows good quantitative agreement with human responses in the determination of the apparent orientations of bars in a variety of bar junctions.

We investigate what is essential for the nonlinear spatial structure of the receptive fields of complex cells, specifically, whether pooling of simple-cell responses or intracellular computations of individual cells are crucial for the spatial structure. The complex cells exhibit spatial nonlinear interactions which most likely represent the organization of afferent receptive fields and their interactions. We estimate "steady" Wiener-like kernels over two-dimensions in space by carrying out network simulations of various models including an energy model and a noncascade model. The cascade models pooling simple-cell responses reproduce physiologically estimated kernels, while a noncascade model fails to reproduce the kernel. The results suggests that the afferent connections from simple cells are crucial for the spatial structure of complex cells.

We propose an algorithm for the determination of three dimensional shape and perspective based on the response of the human visual system to changes in visual textures. Current computer vision algorithms are computationally intensive and show inherent difficulties in integrating additional cues for the determination of shape, such as shading, contour, or motion. In order to develop a fast and simple mechanism less constrained for integrating other cues, we incorporated aspects of the physiological properties of cortical cells in VI into a network model. We provide psychophysical evidence that the local spatial frequency spectrum is represented by the spatially averaged peak frequency (APF). After normalization, this APF measures texture compression and leads to estimates of 3D shape and depth. Simulations of the model show good agreement with human responses to a range of textured images.>