Trace Inference, Curvature Consistency, and Curve Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two stages of curve detection suggest two styles of visual computation
Neural Computation
Spatial decorrelation in orientation-selective cortical cells
Neural Computation
The Perceptual Organization of Texture Flow: A Contextual Inference Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shadows and shading flow fields
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Toward discrete geometric models for early vision
Toward discrete geometric models for early vision
Hue geometry and horizontal connections
Neural Networks - 2004 Special issue Vision and brain
How Close Are We to Understanding V1?
Neural Computation
General Geometric Good Continuation: From Taylor to Laplace via Level Sets
International Journal of Computer Vision
Connection geometry, color, and stereo
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Neural mechanisms for mid-level optical flow pattern detection
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A differential model of the complex cell
Neural Computation
Predictive coding as a model of the V1 saliency map hypothesis
Neural Networks
Tangent bundle curve completion with locally connected parallel networks
Neural Computation
A single functional model of drivers and modulators in cortex
Journal of Computational Neuroscience
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Neurons in primary visual cortex respond selectively to oriented stimuli such as edges and lines. The long-range horizontal connections between them are thought to facilitate contour integration. While many physiological and psychophysical findings suggest that collinear or association field models of good continuation dictate particular projection patterns of horizontal connections to guide this integration process, significant evidence of interactions inconsistent with these hypotheses is accumulating. We first show that natural random variations around the collinear and association field models cannot account for these inconsistencies, a fact that motivates the search for more principled explanations. We then develop a model of long-range projection fields that formalizes good continuation based on differential geometry. The analysis implicates curvature(s) in a fundamental way, and the resulting model explains both consistent data and apparent outliers. It quantitatively predicts the (typically ignored) spread in projection distribution, its nonmonotonic variance, and the differences found among individual neurons. Surprisingly, and for the first time, this model also indicates that texture (and shading) continuation can serve as alternative and complementary functional explanations to contour integration. Because current anatomical data support both (curve and texture) integration models equally and because both are important computationally, new testable predictions are derived to allow their differentiation and identification.