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In this paper we provide rigorous proof for the convergence of an iterative voting-based image segmentation algorithm called Active Masks. Active Masks (AM) was proposed to solve the challenging task of delineating punctate patterns of cells from fluorescence microscope images. Each iteration of AM consists of a linear convolution composed with a nonlinear thresholding; what makes this process special in our case is the presence of additive terms whose role is to "skew" the voting when prior information is available. In real-world implementation, the AM algorithm always converges to a fixed point. We study the behavior of AM rigorously and present a proof of this convergence. The key idea is to formulate AM as a generalized (parallel) majority cellular automaton, adapting proof techniques from discrete dynamical systems.


Sourced from the preprint at arXiv:1102.2615v2 [math.FA].

Date of arXiv submission (preprint version 2): 21 Feb 2011

The publisher version of record at ScienceDirect.

'Date of publication' refers to the publisher version.



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Applied and Computational Harmonic Analysis