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<p>The problem of reinforcing local evidence of linear structure while suppressing unwanted information in noisy images is considered, using a modified form of relaxation labeling. The methodology is based on parametrizing a continuous set of orientation labels via a single vector and using a sigmoidal thresholding function to bias neighborhood influence and ensure convergence to a meaningful stable state. Label strength and label/no-label decisions are incorporated into a single functional. Optimal points of the functional represent the cases where as many pixels (objects) as possible have achieved the desirable linear-structure-reinforced and noise-suppressed labelings. Three different linear structure reinforcement tasks are considered within the general framework: edge reinforcement, edge reinforcement with thinning, and bar (line segment) reinforcement. Results from several image data sets are presented. This approach can directly handle continuous feature information from low-level image analysis operators, and the computational complexity of labeling is reduced.</p>
linear structure reinforcement; label strength; information suppression; neighbourhood influence biassing; bar reinforcement; line segment reinforcement; parametrized relaxation labeling; local evidence; noisy images; orientation labels; sigmoidal thresholding function; convergence; noise-suppressed labelings; edge reinforcement; thinning; computational complexity; computational complexity; picture processing

J. Duncan and T. Birkholzer, "Reinforcement of Linear Structure using Parametrized Relaxation Labeling," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 14, no. , pp. 502-515, 1992.
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