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Fuzzy-based algorithm for color recognition of license plates

机译:基于模糊的车牌颜色识别算法

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Color recognition of license plates plays an important role in a license plate recognition (LPR) system. But it can be a challenging task as the appearances of license plates are affected by various factors such as illumination, camera characteristics, etc. And the color features of license plates in different places may be quite different. To address these concerns, this paper presents an algorithm based on fuzzy logic. The HSV (hue, saturation and value) color space is employed to perform color feature extraction. Three components of the HSV space are firstly mapped to fuzzy sets according to different membership functions. The fuzzy classification function for color recognition is, then, described by the fusion of three weighted membership degrees. For adaptation of the proposed algorithm, we also present a learning algorithm to obtain the correlative parameters. On a DSP-based embedded LPR platform, comparisons were drawn with other classifiers within three sets of test images. Experimental results show that the proposed algorithm achieves higher classification accuracy and better adaptability.
机译:车牌的颜色识别在车牌识别(LPR)系统中起着重要的作用。但是,由于牌照的外观受照明,摄像头特性等各种因素的影响,因此这可能是一项具有挑战性的任务。不同位置的牌照的颜色特征可能会大不相同。为了解决这些问题,本文提出了一种基于模糊逻辑的算法。 HSV(色相,饱和度和值)颜色空间用于执行颜色特征提取。首先根据不同的隶属度函数将HSV空间的三个分量映射到模糊集。然后,通过三个加权隶属度的融合来描述用于颜色识别的模糊分类函数。为了适应所提出的算法,我们还提出了一种学习算法来获取相关参数。在基于DSP的嵌入式LPR平台上,在三组测试图像中与其他分类器进行了比较。实验结果表明,该算法具有较高的分类精度和适应性。

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