CONVERGENCE BEHAVIOR OF THE ACTIVE MASK SEGMENTATION ALGORITHM.
CONVERGENCE BEHAVIOR OF THE ACTIVE MASK SEGMENTATION ALGORITHM.
复制标题
主动掩模分割算法的收敛行为。
DOI：
10.1109/icassp.2010.5495723
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Kovačević,Jelena
中科院分区：
文献类型：
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作者：
Balcan,DoruC;Srinivasa,Gowri;Fickus,Matthew;Kovačević,Jelena
我们研究了主动掩模（AM）框架的收敛行为，该框架最初设计用于分割点状图像模式。AM结合了传统活动轮廓的灵活性，区域增长方法的统计建模能力，以及多尺度和多分辨率方法的计算效率。此外，它实现了实验收敛到零变化（定点）配置，分割算法的理想属性。其核心是一个基于投票的分布函数，其行为类似于多数元胞自动机。本文提出了一个与AM收敛性相关的经验测度，并给出了平滑滤波算子实现收敛的充分理论条件。
We study the convergence behavior of the Active Mask (AM) framework, originally designed for segmenting punctate image patterns. AM combines the flexibility of traditional active contours, the statistical modeling power of region-growing methods, and the computational efficiency of multiscale and multiresolution methods. Additionally, it achieves experimental convergence to zero-change (fixed-point) configurations, a desirable property for segmentation algorithms. At its a core lies a voting-based distributing function which behaves as a majority cellular automaton. This paper proposes an empirical measure correlated to the convergence behavior of AM, and provides sufficient theoretical conditions on the smoothing filter operator to enforce convergence.