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Boundary Detection by Constrained Optimization
July 1990 (vol. 12 no. 7)
pp. 609-628

A statistical framework is used for finding boundaries and for partitioning scenes into homogeneous regions. The model is a joint probability distribution for the array of pixel gray levels and an array of labels. In boundary finding, the labels are binary, zero, or one, representing the absence or presence of boundary elements. In partitioning, the label values are generic: two labels are the same when the corresponding scene locations are considered to belong to the same region. The distribution incorporates a measure of disparity between certain spatial features of block pairs of pixel gray levels, using the Kolmogorov-Smirnov nonparametric measures of difference between the distributions of these features. The number of model parameters is minimized by forbidding label configurations, which are assigned probability zero. The maximum a posteriori estimator of boundary placements and partitionings is examined. The forbidden states introduce constraints into the calculation of these configurations. Stochastic relaxation methods are extended to accommodate constrained optimization.

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Index Terms:
scenes partitioning; boundary detection; stochastic relaxation; constrained optimization; probability distribution; pixel gray levels; scene locations; Kolmogorov-Smirnov; forbidding label configurations; boundary placements; optimisation; pattern recognition; picture processing; statistical analysis
Citation:
D. Geman, S. Geman, C. Graffigne, P. Dong, "Boundary Detection by Constrained Optimization," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 609-628, July 1990, doi:10.1109/34.56204
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