Trace Inference, Curvature Consistency, and Curve Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Robust Classifiers by Mixed Adaptation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deriving stopping rules for the probabilistic Hough transform by sequential analysis
Computer Vision and Image Understanding
A New Probabilistic Relaxation Scheme and Its Application to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Generic Grouping Algorithm and Its Quantitative Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Grouping-Based Nonadditive Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Figure-Ground Discrimination: A Combinatorial Optimization Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantitative Analysis of Grouping Processes
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Stochastic completion fields: a neural model of illusory contour shape and salience
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Probabilistic Interpretation of the Saliency Network
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Object-level structured contour map extraction
Computer Vision and Image Understanding
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This paper proposes a new, efficient, figure from ground method. At every stage the data features are classified to either "background" or "unknown yet" classes, thus emphasizing the background detection task (and implying the name of the method). The sequential application of such classification stages creates a bootstrap mechanism which improves performance in very cluttered scenes. This method can be applied to many perceptual grouping cues, and an application to smoothness-based classification of edge points is given. A fast implementation using a kd-tree allows to work on large, realistic images.