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
Population coding of stimulus orientation by striate cortical cells
Biological Cybernetics
Potentials, valleys, and dynamic global coverings
International Journal of Computer Vision
Shunting inhibition does not have a divisive effect on firing rates
Neural Computation
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Junctions: Detection, Classification, and Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic interpretation of population codes
Neural Computation
A neural model of contour integration in the primary visual cortex
Neural Computation
Robust Image Corner Detection Through Curvature Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Introduction to Neural and Cognitive Modeling
Introduction to Neural and Cognitive Modeling
Recurrent long-range interactions in early vision
Emergent neural computational architectures based on neuroscience
Neural mechanisms for representing surface and contour features
Emergent neural computational architectures based on neuroscience
A Biologically Motivated Scheme for Robust Junction Detection
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Evaluation of Corner Extraction Schemes Using Invariance Methods
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
A simple cell model with dominating opponent inhibition for robust image processing
Neural Networks - 2004 Special issue Vision and brain
Disambiguating Visual Motion Through Contextual Feedback Modulation
Neural Computation
Disambiguating Visual Motion by Form-Motion Interaction--a Computational Model
International Journal of Computer Vision
Iterated tensor voting and curvature improvement
Signal Processing
CONFIGR: A vision-based model for long-range figure completion
Neural Networks
Spatial-Temporal Junction Extraction and Semantic Interpretation
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
An Approach to the Parameterization of Structure for Fast Categorization
International Journal of Computer Vision
Neural mechanisms for form and motion detection and integration: biology meets machine vision
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Biological models for active vision: towards a unified architecture
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
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Junctions provide important cues in various perceptual tasks, such as the determination of occlusion relationships for figure-ground separation, transparency perception, and object recognition, among others. In computer vision, junctions are used in a number of tasks, like point matching for image tracking or correspondence analysis. We propose a biologically motivated approach to junction representation in which junctions are implicitly characterized by high activity for multiple orientations within a cortical hypercolumn. A local measure of circular variance is suggested to extract junction points from this distributed representation. Initial orientation measurements are often fragmented and noisy. A coherent contour representation can be generated by a model of V1 utilizing mechanisms of collinear long-range integration and recurrent interaction. In the model, local oriented contrast estimates that are consistent within a more global context are enhanced while inconsistent activities are suppressed. In a series of computational experiments, we compare junction detection based on the new recurrent model with a feedforward model of complex cells. We show that localization accuracy and positive correctness in the detection of generic junction configurations such as L- and T-junctions is improved by the recurrent long-range interaction. Further, receiver operating characteristics analysis is used to evaluate the detection performance on both synthetic and camera images, showing the superior performance of the new approach. Overall, we propose that nonlocal interactions implemented by known mechanisms within V1 play an important role in detecting higher-order features such as corners and junctions.