Two stages of curve detection suggest two styles of visual computation
Neural Computation
Two-dimensional and three-dimensional texture processing in visual cortex of the macaque monkey
Early vision and beyond
A neural model of contour integration in the primary visual cortex
Neural Computation
A V1 model of pop out and asymmetry in visual search
Proceedings of the 1998 conference on Advances in neural information processing systems II
Biophysiologically plausible implementations of the maximum operation
Neural Computation
A multiple bit upset tolerant SRAM memory
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Neural mechanisms for the robust representation of junctions
Neural Computation
Shape Saliency Modulates Contextual Processing in the Human Lateral Occipital Complex
Journal of Cognitive Neuroscience
State-dependent computation using coupled recurrent networks
Neural Computation
Learning in computer vision: some thoughts
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Optical Memory and Neural Networks
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Recurrent interactions in the primary visual cortex make its output a complex nonlinear transform of its input. This transform serves preattentive visual segmentation, that is, autonomously processing visual inputs to give outputs that selectively emphasize certain features for segmentation. An analytical understanding of the nonlinear dynamics of the recurrent neural circuit is essential to harness its computational power. We derive requirements on the neural architecture, components, and connection weights of a biologically plausible model of the cortex such that region segmentation, figure-ground segregation, and contour enhancement can be achieved simultaneously. In addition, we analyze the conditions governing neural oscillations, illusory contours, and the absence of visual hallucinations. Many of our analytical techniques can be applied to other recurrent networks with translation-invariant neural and connection structures.