1 July 1996 Adaptive reconstructive τ-openings: convergence and the steady-state distribution
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A parameterized t-opening is a filter defined as a union of openings by a collection of compact, convex structuring elements, each scalar multiplied by the parameter. For a reconstructive t-opening, the filter is modified by fully passing any connected component not completely eliminated. Applied to the signal-union-noise model, in which the reconstructive filter is designed to sieve out clutter while passing the signal, the optimization problem is to find a parameter value that minimizes the MAE between the filtered and ideal image processes. The present study introduces an adaptation procedure for the design of reconstructive t-openings. The adaptive filter fits into the framework of Markov processes, the adaptive parameter being the state of the process. There exists a stationary distribution governing the parameter in the steady state and convergence is characterized via the steady-state distribution. Key filter properties such as parameter mean, parameter variance, and expected error in the steady state are characterized via the stationary distribution. The Chapman-Kolmogorov equations are developed for various scanning modes and transient behavior is examined.
Yidong Chen, Yidong Chen, Edward R. Dougherty, Edward R. Dougherty, "Adaptive reconstructive τ-openings: convergence and the steady-state distribution," Journal of Electronic Imaging 5(3), (1 July 1996). https://doi.org/10.1117/12.244908

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