Detecting Directionality in Random Fields Using the Monogenic Signal
Abstract
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 non-directional 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 2D 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 non-unidirectional random fields, which allows the automatic classification of images.
Cite
@article{arxiv.1304.2998,
title = {Detecting Directionality in Random Fields Using the Monogenic Signal},
author = {Sofia Olhede and David Ramírez and Peter J. Schreier},
journal= {arXiv preprint arXiv:1304.2998},
year = {2021}
}