Regularization of inverse visual problems involving discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
The stability and control of discrete processes
The stability and control of discrete processes
Relaxation labelling algorithms-a review
Image and Vision Computing
On compatibility and support functions in probabilistic relaxation
Computer Vision, Graphics, and Image Processing
A Simplex-Like Algorithm for the Relaxation Labeling Process
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge-Labeling Using Dictionary-Based Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reinforcement of Linear Structure using Parametrized Relaxation Labeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relaxation by the Hopfield neural network
Pattern Recognition
Active vision
Probabilistic relaxation as an optimizer
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
The Dynamics of Nonlinear Relaxation Labeling Processes
Journal of Mathematical Imaging and Vision
Geometry-Driven Diffusion in Computer Vision
Geometry-Driven Diffusion in Computer Vision
Bayesian Modeling of Uncertainty in Low-Level Vision
Bayesian Modeling of Uncertainty in Low-Level Vision
Learning Compatibility Coefficients for Relaxation Labeling Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Algorithm for Probabilistic Relaxation Based on the Baum Eagon Theorem
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
On representation and matching of multi-coloured objects
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
The Dynamics of Nonlinear Relaxation Labeling Processes
Journal of Mathematical Imaging and Vision
Continuous-Time Relaxation Labeling Processes
EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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Traditional probabilistic relaxation, as proposed byRosenfeld, Hummel and Zucker, uses a support functionwhich is a double sum over neighboring nodes and labels.Recently, Pelillo has shown the relevance of the Baum-Eagon theoremto the traditional formulation. Traditional probabilistic relaxationis now well understood in an optimization framework.Kittler and Hancock have suggested a form of probabilistic relaxation with product support, based on an evidence combining formula.In this paper we present a formal basis for Kittler and Hancocksprobabilistic relaxation. We show that it too has close linkswith the Baum-Eagon theorem, and may be understood in an optimizationframework. We provide some proofs to show that a stable stationarypoint must be a local maximum of an objective function.We present a new form of probabilistic relaxation that can be used as an approximate maximizerof the global labeling with maximum posterior probability.