三种形态分析方法的比较。化学品运输船案例研究

E. A. Monroy-Sahade, M. Calderón-Ramírez, A. Espinosa-Calderón, Rafael Garcia-Arredondo, J. Padilla-Medina, E. Gramsch-Labra
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摘要

本文的目的是比较三种图像处理技术在陆地化学品突发事件中识别油轮的性能。最好的算法将成为未来处理化学品紧急情况的电子平台的一部分。这种平台应该是便携式的,并且应该允许它安装在不同的设备上,比如无人机。为了真实起见,这些图像是用无人机获得的。通过三种方法对图像进行处理:Hotelling变换、Hu矩不变量和Signature变换(距离与角度)。结果表明,胡法是最准确的方法,在测试中获得86%的准确率;其次是签名,占71%;在这种情况下,不太可靠的是霍特林,只有23%。未来的平台将不会是自主的,因此它将始终由操作人员监督,只在决策过程中帮助他们。由于这只是一个更大研究的一小部分,未来还有很多工作要做。将增加数据库,并对算法性能的未来改进进行测试。然而,随着识别算法的工作,本文的目标已经实现,并且具有足够的精度,达到了它的目的。
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Comparation of Three Methods for Morphological Analysis. Case Study of Chemical Transporting Tankers
The aim of this article is to compare the performance of three image processing techniques for recognizing the tanker in land chemical emergencies. The best algorithm will be part of a future electronic platform for attending chemical emergencies. Such platform should be portable and should allow its installation in different devices, i.e. drones. To be realistic, the images were obtained with a drone. These images were processed via three methods: Hotelling transform, Hu moment invariants, and Signature transform (Distance vs. Angle). The results showed that Hu was the most accurate method, obtaining 86% of accuracy in the tests; followed by Signature with 71%; and the less reliable in this case was Hotelling, with only a 23%. The future platform will not be autonomous, so it will always be supervised by an operator and only help them in the decision-making process. Since this is a small piece of a bigger research, it still has a lot of future work. Data base will be increased, and future improvements in the performance of the algorithm will be tested. Nevertheless, the objective of this paper was achieved as the recognition algorithm worked, with enough accuracy, for the purpose it was aimed to.
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