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1D Barcode Region Detection Based on the Hough Transform and Support Vector Machine

机译:1D条形码区域检测基于Hough变换和支持向量机

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The barcode is widely used in logistics, identification, and other applications. Most of the research and applications now focus on how to decode the barcode. However, in a complex situation, the barcode is difficult to locate accurately. This paper presents an algorithm that can effectively detect the locations of multiple barcodes. First, image texture features are extracted by combining the local binary pattern (LBP) and gray histogram, and then a machine learning algorithm is applied to create a classifier by using the support vector machine (SVM) to extract and train the positive and negative samples. Second, the Hough transform is applied to the input image to achieve the angle invariable. Finally, our proposed method has been evaluated by the WWU Muenstar Barcode Database and the experimental results show that the proposed result has higher performance than other methods.
机译:条形码广泛用于物流,识别和其他应用中。大多数研究和应用程序现在关注如何解码条形码。然而,在复杂的情况下,条形码很难准确定位。本文介绍了一种可以有效地检测多个条形码的位置的算法。首先,通过组合局部二进制模式(LBP)和灰度直方图来提取图像纹理特征,然后应用机器学习算法通过使用支持向量机(SVM)来创建分类器来提取和培训正和阴性样本。其次,霍夫变换应用于输入图像以实现角度不变。最后,我们提出的方法由WWU Muenstar条形码数据库评估,实验结果表明,所提出的结果具有比其他方法更高的性能。

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