统计图像处理的计算效率算法。在R中实现
Computationally efficient algorithms for statistical image processing. Implementation in R
摘要：
在我们早期的系列论文中，我们提出了一种新的统计假设检验方法，用于检测噪声图像中的目标。该方法使用渗流理论和随机图论的结果。我们开发的算法，允许检测未知形状的对象中存在的非参数噪声的未知水平和未知的分布。没有边界形状的约束施加在对象上，只有一个弱的体积条件的对象的内部是必需的。我们的算法具有线性复杂度和指数精度。在本文中，我们描述了我们的非参数假设检验方法的实现。我们提供了一个可用于图像处理中统计实验的程序。该程序是用统计编程语言R编写的。
英文摘要：
In the series of our earlier papers on the subject, we proposed a novel statistical hypothesis testing method for detection of objects in noisy images. The method uses results from percolation theory and random graph theory. We developed algorithms that allowed to detect objects of unknown shapes in the presence of nonparametric noise of unknown level and of unknown distribution. No boundary shape constraints were imposed on the objects, only a weak bulk condition for the object's interior was required. Our algorithms have linear complexity and exponential accuracy. In the present paper, we describe an implementation of our nonparametric hypothesis testing method. We provide a program that can be used for statistical experiments in image processing. This program is written in the statistical programming language R.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
