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IEEE Transactions on Pattern Analysis and Machine Intelligence
Relaxation labelling algorithms-a review
Image and Vision Computing
Optimisation algorithms in probabilistic relaxation labelling
Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications
On the stability of the travelling salesman problem algorithm of Hopfield and Tank
Biological Cybernetics
A parallel architecture for relaxation operations
Pattern Recognition
Image labelling: a neural network approach
Image and Vision Computing
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Relaxation and neural learning: points of convergence and divergence
Journal of Parallel and Distributed Computing - Neural Computing
Accelerated learning in layered neural networks
Complex Systems
Radial Projection: An Efficient Update Rule for Relaxation Labeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Simplex-Like Algorithm for the Relaxation Labeling Process
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two stages of curve detection suggest two styles of visual computation
Neural Computation
A parallel architecture for probabilistic relaxation operations on images
Pattern Recognition
Learning algorithms and networks of neurons
The computing neuron
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Learning and relearning in Boltzmann machines
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Relaxation by the Hopfield neural network
Pattern Recognition
Breaking substitution ciphers using a relaxation algorithm
Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parameter Estimation for Optimal Object Recognition: Theory andApplication
International Journal of Computer Vision
The Dynamics of Nonlinear Relaxation Labeling Processes
Journal of Mathematical Imaging and Vision
On the Foundations of Probabilistic Relaxationwith Product Support
Journal of Mathematical Imaging and Vision
A Machine Learning Approach to POS Tagging
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A RKHS Interpolator-Based Graph Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Driven Generation of Interactions for Feature Binding and Relaxation Labeling
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Continuous-Time Relaxation Labeling Processes
EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Adaptive Pixel-Based Data Fusion for Boundary Detection
EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
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
Learning to Form Large Groups of Salient Image Features
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Semantic graph and arc consistency in "true" three dimensional image labelling
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
POS tagging using relaxation labelling
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
AI Communications - Constraint Programming for Planning and Scheduling
Probabilistic relaxation labelling using the Fokker-Planck equation
Pattern Recognition
Probabilistic relaxation labeling by Fokker-Planck diffusion on a graph
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
A graph spectral approach to consistent labelling
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Kernel spectral correspondence matching using label consistency constraints
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Improving correspondence matching using label consistency constraints
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
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Relaxation labeling processes have been widely used in many different domains including image processing, pattern recognition, and artificial intelligence. They are iterative procedures that aim at reducing local ambiguities and achieving global consistency through a parallel exploitation of contextual information, which is quantitatively expressed in terms of a set of "compatibility coefficients." The problem of determining compatibility coefficients has received a considerable attention in the past and many heuristic, statistical-based methods have been suggested. In this paper, the authors propose a rather different viewpoint to solve this problem: they derive them attempting to optimize the performance of the relaxation algorithm over a sample of training data; no statistical interpretation is given: compatibility coefficients are simply interpreted as real numbers, for which performance is optimal. Experimental results over a novel application of relaxation are given, which prove the effectiveness of the proposed approach.