An Augmented Lagrangian Model for Signal Segmentation

Abstract In this paper, we provide a new insight to the two-phase signal segmentation problem. We propose an augmented Lagrangian variational model based on Chan–Vese’s original one. Using both energy methods and PDE methods, show, in one-dimensional case, that set of minimizers proposed functional contains only binary functions it coincides with This fact allows us obtain two important features as by-product our analysis. First all, for piecewise constant initial signal, jump any minimizer is subset given signal. Second, all points belong same level multivalued sense. last property permits design trivial algorithm computing minimizers.