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A New Pixel-Level Background Subtraction Algorithm in Machine Vision

机译:机器视觉中一种新的像素级背景减除算法

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In the machine vision, the algorithm of background subtraction is used in target detection widely. In this paper, we reproduce some algorithms such as ViBe, Local Binary Similar Pattern (LBSP), Local Ternary Pattern (LTP) and so on. In view of the problem of inaccurate edge in target detection, we propose a method of combining color and LBSP for background model. It can obtain information both in pixel and texture (marked as improved-LBSP in the paper). On this basis, we propose a new method marked as BFs-method in the paper, which have a new persistence consists of color, LBSP, and time (t). The key advantage of this method lie in its highly robust dictionary model as well as it's ability to automatically adjust pixel-level segmentation behavior, which improves the ability to remove the shadow of the target and the hole inside the target.
机译:在机器视觉中,背景减除算法被广泛应用于目标检测中。在本文中,我们重现了一些算法,例如ViBe,局部二进制相似模式(LBSP),局部三进制模式(LTP)等。针对目标检测中边缘不准确的问题,我们提出了一种将颜色和LBSP相结合的背景模型方法。它可以获得像素和纹理方面的信息(在本文中标记为改进的LBSP)。在此基础上,我们提出了一种新的标记为BFs方法的方法,该方法具有由颜色,LBSP和时间(t)组成的新持久性。该方法的主要优势在于其高度健壮的字典模型以及自动调整像素级分割行为的能力,从而提高了去除目标阴影和目标内部孔洞的能力。

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