A STATISTICAL APPROACH TO IDENTIFYING CLOSED OBJECT BOUNDARIES IN IMAGES
A STATISTICAL APPROACH TO IDENTIFYING CLOSED OBJECT BOUNDARIES IN IMAGES
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DOI：
10.2307/1427893
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发表时间：
1994-12-01
影响因子：
1.2
通讯作者：
DAVIDSON, JL
中科院分区：
文献类型：
--
作者：
HELTERBRAND, JD;CRESSIE, N;DAVIDSON, JL
在这项研究中，我们提出了一个统计理论，并提出了一个算法，以确定一个像素宽的封闭物体边界的灰度图像。封闭边界识别是一个重要的问题，因为目标的边界是图像中的主要特征。尽管如此，大多数图像恢复和纹理识别的统计方法在图像域上放置了不适当的静态模型假设。表征图像中存在的结构成分的一种方式是识别描绘对象的一个像素宽的闭合边界。通过定义一个先验概率模型的空间上的一个像素宽的封闭边界配置和适当地指定转移概率函数在这个空间上，马尔可夫链蒙特卡罗算法构造，理论上收敛到一个统计上最优的封闭边界估计。此外，这种方法确保了统计最优边界估计的任何近似都具有必要的封闭性。
In this research, we present a statistical theory, and an algorithm; to identify one-pixel-wide closed object boundaries in gray-scale images. Closed-boundary identification is an important problem because boundaries of objects are major features in images. In spite of this, most statistical approaches to image restoration and texture identification place inappropriate stationary model assumptions on the image domain. One way to characterize the structural components present in images is to identify one-pixel-wide closed boundaries that delineate objects. By defining a prior probability model on the space of one-pixel-wide closed boundary configurations and appropriately specifying transition probability functions on this space, a Markov chain Monte Carlo algorithm is constructed that theoretically converges to a statistically optimal closed boundary estimate. Moreover, this approach ensures that any approximation to the statistically optimal boundary estimate will have the necessary property of closure.