The study of solar coronal holes (CHs) is important in the understanding of solar physics and the prediction of space weather events, which have significant impact on space-based instruments, communication and navigation systems. With the availability of the multi-wavelength Atmospheric Imaging Assembly (AIA) instrument on board Solar Dynamics Observatory (SDO) satellite, a large volume of high-resolution solar images are produced continuously. Proper detection of CHs from AIA images is an important issue and recently, a few contour and machine learning-based techniques are found to be promising for such purpose. However, accuracy, time complexity and the requirement of human intervention are some of the critical issues with such methods. In this paper, to address these challenging issues, two contour-based approaches are developed, namely i) the Hough transformed simulated parameterized online region-based active contour method (POR-ACM) and ii) fast fuzzy c-means clustering followed by Hough transformed simulated static contour method (FFCM-SCM). The major issues that are addressed here are automated initialization of contour, reducing time complexity and avoidance of non-coronal hole inside a coronal hole region during contour evolution. The proposed techniques have been tested on three benchmark solar disk images and compared with the existing active contour without edge- (ACWE) based method and fuzzy energy-based dual contour method (FEDCM) of CHs segmentation. The results indicate the capability of the proposed techniques in detection and extraction of CHs in solar disk image with higher accuracy and reduced time complexity.

中文翻译：

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两种基于轮廓的太阳图像日冕洞检测图像分割技术的比较研究与发展

日冕洞（CHs）的研究对于理解太阳物理和空间天气事件的预​​测具有重要意义，对天基仪器、通信和导航系统具有重要影响。随着太阳动力学天文台 (SDO) 卫星上的多波长大气成像组件 (AIA) 仪器的使用，连续生成大量高分辨率太阳图像。从 AIA 图像中正确检测 CH 是一个重要问题，最近，一些基于轮廓和机器学习的技术被发现有希望用于此目的。然而，准确性、时间复杂度和人工干预的要求是此类方法的一些关键问题。在本文中，为了解决这些具有挑战性的问题，开发了两种基于轮廓的方法，即 i) 霍夫变换模拟参数化在线基于区域的活动轮廓方法 (POR-ACM) 和 ii) 快速模糊 c 均值聚类，然后是霍夫变换模拟静态轮廓方法 (FFCM-SCM)。这里解决的主要问题是轮廓的自动初始化、降低时间复杂度和在轮廓演化过程中避免冠状孔区域内的非冠状孔。所提出的技术已经在三个基准太阳盘图像上进行了测试，并与现有的无边缘活动轮廓（ACWE）方法和基于模糊能量的双轮廓方法（FEDCM）的CHs分割进行了比较。结果表明，所提出的技术能够以更高的精度和更低的时间复杂度检测和提取太阳盘图像中的CHs。