Inferring global perceptual contours from local features
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
A neural model of contour integration in the primary visual cortex
Neural Computation
Perceptual groupings in a self-organizing map of spiking neurons
Perceptual groupings in a self-organizing map of spiking neurons
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Early Cognitive Vision: Using Gestalt-Laws for Task-Dependent, Active Image-Processing
Natural Computing: an international journal
Hierarchial self-organization of minicolumnar receptive fields
Neural Networks - 2004 Special issue: New developments in self-organizing systems
A Proto-object Based Visual Attention Model
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
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The Gestalt principle of collinearity (and curvilinearity) is widely regarded as being mediated by the long-range connection structure in primary visual cortex. We review the neurophysiological and psychophysical literature to argue that these connections are developed from visual experience after birth, relying on coherent object motion. We then present a neural network model that learns these connections in an unsupervised Hebbian fashion with input from real camera sequences. The model uses spatiotemporal retinal filtering, which is very sensitive to changes in the visual input. We show that it is crucial for successful learning to use the correlation of the transient responses instead of the sustained ones. As a consequence, learning works best with video sequences of moving objects. The model addresses a special case of the fundamental question of what represents the necessary a priori knowledge the brain is equipped with at birth so that the self-organized process of structuring by experience can be successful.