Mean-Shift算法在车牌定位中的应用

K. Shah, Harsh K. Kapadia, V. Shah, Maurya Shah
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引用次数: 6

摘要

本文用不同的方法讨论了车牌图像的定位问题。重点介绍了均值移位算法,这是一种非参数特征空间分析技术。它在每个数据点周围定义一个窗口,并通过mean - shift Vector将窗口的中心移动到数据点的平均值,直到它收敛并且窗口移动到更密集的区域。其他方法包括裁剪对象、形态学操作、阈值法和对象面积法。我们还讨论了Mean-Shift算法如何足够精确和鲁棒,以用于同一区域。这包括算法的基本定义和数学方程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Mean-Shift algorithm for license plate localization
This paper discusses about localization of license plate from vehicle images using different methods. And main focus is on Mean Shift Algorithm which is a non parametric feature space analysis technique. It defines a window around each data points and shifts the center of the window to the mean of the data point though a Mean-Shift Vector till it converges and window shifts to the denser region. The other methods used are Crop Object, Morphological operations, Localize the object by thresholding and area of object. We also discuss how Mean-Shift algorithm is accurate enough and robust to be used in the same area. This includes the basic definition of the algorithm and mathematical equations.
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