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Cat Swarm Based Shadow Detection Method

机译:基于猫群的阴影检测方法

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摘要

Innovations brought by developing technology are used in different forms in security area. Those technologies on imaging are usually involved in monitoring, tracking and detecting. When these processes are performed, the shadow of the object can prevent detection and monitoring. Therefore, in this study, a method has been proposed to determine the shadows of the objects and to distinguish them from the original image, and to determine the real image of the object. Previous work on this area has been done using classic shadow detection methods. In order to overcome their disadvantages during the detection period, this study uses cat swarm optimization, which can be applied to many problems. With this method, the shadows belonging to the object are detected, and the objects are determined by separating the shadows from the image. Later, if desired, follow-up of the object will also be achieved. It is shown that the suggested cat swarm optimization algorithm provides an effective result in shadow detection and give better results with shadow detection rate over compared algorithms.
机译:开发技术带来的创新在安全领域以不同形式使用。这些成像技术通常涉及监视,跟踪和检测。执行这些过程时,对象的阴影可能会阻止检测和监视。因此,在这项研究中,已经提出了一种确定物体的阴影并将其与原始图像区分开并确定物体的真实图像的方法。使用经典阴影检测方法已经完成了该领域的先前工作。为了克服它们在检测期间的缺点,本研究使用猫群优化,可以将其应用于许多问题。通过这种方法,检测出属于对象的阴影,并通过将阴影与图像分离来确定对象。以后,如果需要的话,还可以实现对目标的跟踪。结果表明,所提出的猫群优化算法在阴影检测中提供了有效的结果,并且与比较算法相比,阴影检测率更高。

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