Trace Inference, Curvature Consistency, and Curve Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Trace Inference, Curvature Consistency, and Curve Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Dynamics of Nonlinear Relaxation Labeling Processes
Journal of Mathematical Imaging and Vision
Learning Compatibility Coefficients for Relaxation Labeling Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Based Probabilistic Relaxation for Sub-pixel Contour Extraction
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
On the Distribution of Saliency
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Relaxation labeling uses contextual information for finding consistent labelings of graphs. Although relaxation labeling is parallel and iterative, the complexity of updating with standard rules is too costly for practical implementation. A description is given of a computationally more efficient updating rule that utilizes radial projection instead of normal projection to avoid the complexities incurred by previous update rules when boundaries to the labeling space are encountered. This reduction in complexity is achieved by first restricting support vectors to the positive quadrant, and then using radial projection onto the constraint instead of normal projection. Crucial order information is conserved through smooth convergence towards the optimum and a rate of convergence proportional to the magnitudes of the support functions.