Relaxation labelling algorithms-a review
Image and Vision Computing
Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition
Perspectives on the theory and practice of belief functions
International Journal of Approximate Reasoning
Robust Contour Decomposition Using a Constant Curvature Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fuzzy relaxation technique for partial shape matching
Pattern Recognition Letters
On the Foundations of Probabilistic Relaxationwith Product Support
Journal of Mathematical Imaging and Vision
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Fuzzy Measure Theory
Relational Matching
Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric Segmentation of Curves into Various Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction
Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction
Globally Consistent Reconstruction of Ripped-Up Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic relaxation labelling using the Fokker-Planck equation
Pattern Recognition
Semi-supervised probabilistic relaxation for image segmentation
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
A graph spectral approach to consistent labelling
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Semi-supervised classification by probabilistic relaxation
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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While various approaches are suggested in the literature to describe and generalize relaxation processes concerning to several objectives, the wider problem addressed here is to find the best-suited relaxation process for a given assignment problem, or better still, to construct a task-dependent relaxation process. For this, we develop a general framework for the theoretical foundations of relaxation processes in pattern recognition. The resulting structure enables 1) a description of all known relaxation processes in general terms and 2) the design of task-dependent relaxation processes. We show that the well-known standard relaxation formulas verify our approach. Referring to the common problem of generating a generalized description of a contour we demonstrate the applicability of the suggested generalization in detail. Important characteristics of the constructed task-dependent relaxation process are: 1) the independency of the segmentation from any parameters, 2) the invariance to geometric transformations, 3) the simplicity, and 4) efficiency.