Title
Guaranteeing Convergence of Iterative Skewed Voting Algorithms for Image Segmentation.
Abstract
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.
Year
DOI
Venue
2011
10.1016/j.acha.2012.03.008
Applied and Computational Harmonic Analysis
Keywords
Field
DocType
Active Masks,Cellular automata,Convergence,Segmentation
Convergence (routing),Cellular automaton,Convolution,Computer science,Segmentation,Algorithm,Image segmentation,Theoretical computer science,Dynamical systems theory,Thresholding,Fixed point
Journal
Volume
Issue
ISSN
33
2
1063-5203
Citations 
PageRank 
References 
1
0.35
5
Authors
4
Name
Order
Citations
PageRank
Doru-Cristian Balcan110.35
Gowri Srinivasa2627.69
M. Fickus350.80
Jelena Kovačević410.35