Pixel isotropy test based on directional perimeters

IF 2.1 2区 数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY
Mariem Abaach , Hermine Biermé , Elena Di Bernardino , Anne Estrade
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引用次数: 0

Abstract

In this paper we consider the so-called directional perimeters of a thresholded gray-level image. These geometrical quantities are built by considering separately the horizontal and vertical contributions of the pixel. We explicitly compute the first two moments of the directional perimeter under the hypothesis of an underlying discrete Gaussian stationary random field. We establish a central limit theorem (CLT), as the number of pixels goes to infinity, for the joint directional perimeters at various levels under a weak summability condition of the covariance function. By using the CLT previously established, we construct a consistent pixel isotropy test, based on the ratio of the directional perimeters. Our theoretical study is completed by extensive numerical illustrations based on simulated data. Finally, we apply our method to detect pixel anisotropy in calcaneus X-ray images.
基于方向周长的像素各向同性测试
在本文中,我们考虑的是阈值灰度图像的所谓方向周长。这些几何量是通过分别考虑像素的水平和垂直贡献而建立的。在底层离散高斯静态随机场的假设下,我们明确计算了方向周长的前两个矩。在协方差函数的弱求和条件下,当像素数达到无穷大时,我们建立了各层次联合方向周长的中心极限定理(CLT)。利用之前建立的 CLT,我们构建了一个基于方向周长比的一致像素各向同性检验。基于模拟数据的大量数值说明完成了我们的理论研究。最后,我们将我们的方法应用于检测小腿X光图像中的像素各向异性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Spatial Statistics
Spatial Statistics GEOSCIENCES, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
CiteScore
4.00
自引率
21.70%
发文量
89
审稿时长
55 days
期刊介绍: Spatial Statistics publishes articles on the theory and application of spatial and spatio-temporal statistics. It favours manuscripts that present theory generated by new applications, or in which new theory is applied to an important practical case. A purely theoretical study will only rarely be accepted. Pure case studies without methodological development are not acceptable for publication. Spatial statistics concerns the quantitative analysis of spatial and spatio-temporal data, including their statistical dependencies, accuracy and uncertainties. Methodology for spatial statistics is typically found in probability theory, stochastic modelling and mathematical statistics as well as in information science. Spatial statistics is used in mapping, assessing spatial data quality, sampling design optimisation, modelling of dependence structures, and drawing of valid inference from a limited set of spatio-temporal data.
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