30 June 1994 Application of probabilistic and dictionary-based relaxation techniques to a statistical method of edge detection
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Abstract
Two relaxation schemes, a probabilistic and a dictionary-based one, applied to edge detection in images are described. The problem of local edge detection is defined by using a statistical approach. The solution, in terms of statistical decision theory, leads to a multiple, composite, overlapping testing problem that involves configurations of sets of four pixels (quadriplets). The relaxation schemes are also developed using the quadriplets as labeling objects. The initial probabilities for the label set of each object are obtained from the conditional risks given by the local statistical tests. The interaction neighborhood adopted for the two methods is the 4- neighborhood. The iterative label probability updating is performed using a classical heuristic procedure in the two schemes. Tests using noisy synthetic and real images are presented. An experimental analysis of convergence to a consistent and non-ambiguous labeling and speed of convergence is performed for the two schemes and the results are compared. A change in the dictionary according to a modification in the definition of consistency is proposed and the resulting scheme is tested and compared with the two other ones.
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Nelson D. A. Mascarenhas, Nelson D. A. Mascarenhas, Andre H.H. Alves, Andre H.H. Alves, } "Application of probabilistic and dictionary-based relaxation techniques to a statistical method of edge detection", Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994); doi: 10.1117/12.179227; https://doi.org/10.1117/12.179227
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