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Reinforcement of linear structure using parametrized relaxation labeling

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2 Author(s)
J. S. Duncan ; Dept. of Electr. Eng., Yale Univ., New Haven, CT, USA ; T. Birkholzer

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

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IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:14 ,  Issue: 5 )