Detecting Directionality in Random Fields Using the Monogenic Signal
Detecting Directionality in Random Fields Using the Monogenic Signal
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DOI：
10.1109/tit.2014.2342734
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发表时间：
2013-04
影响因子：
2.5
通讯作者：
S. Olhede;D. Ramírez;P. Schreier
中科院分区：
文献类型：
--
作者：
S. Olhede;D. Ramírez;P. Schreier
检测和分析图像中的方向结构在许多应用中是重要的，因为一维模式通常对应于重要的特征，如物体轮廓或轨迹。将结构分类为方向性或非方向性需要一个量化方向性程度的度量和一个阈值，阈值需要根据图像的统计来选择。为了做到这一点，我们将图像建模为一个随机场。到目前为止，对随机场的方向性分析的研究还很少。在本文中，我们提出了一种基于随机单基因信号量化方向性程度的方法，该方法可以将二维信号独特地分解为局部幅度、局部方向和局部相位。研究了各向同性、各向异性和单向随机场单基因信号的二阶统计性质。我们分析了有限大小样本图像的方向性度量，并确定了区分单向和非单向随机场的阈值，从而允许图像的自动分类。
Detecting and analyzing directional structures in images is important in many applications since one-dimensional patterns often correspond to important features such as object contours or trajectories. Classifying a structure as directional or nondirectional requires a measure to quantify the degree of directionality and a threshold, which needs to be chosen based on the statistics of the image. In order to do this, we model the image as a random field. So far, little research has been performed on analyzing directionality in random fields. In this paper, we propose a measure to quantify the degree of directionality based on the random monogenic signal, which enables a unique decomposition of a 2-D signal into local amplitude, local orientation, and local phase. We investigate the second-order statistical properties of the monogenic signal for isotropic, anisotropic, and unidirectional random fields. We analyze our measure of directionality for finite-size sample images and determine a threshold to distinguish between unidirectional and nonunidirectional random fields, which allows the automatic classification of images.