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Circular Road Signs Recognition with Affine Moment Invariants and the Probabilistic Neural Classifier

机译:仿射矩不变性和概率神经分类器的圆形路标识别

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In this paper the neural classifier for recognition of the circular shaped road signs is presented. This classifier belongs to the road signs recognition module, which in turn is a part of a driver assisting system. The circular shaped prohibition and obligation signs constitute the very important groups within the set of road signs. In this case however, it is not possible for a detector to determine rotation of the shapes that would allow dimension reduction of the search space. Thus the classifier has to be able to properly work with all possible affine deformations. To alleviate this problem we propose to use as features the statistical moments which were shown to be invariant within an affine group of transformations. The classification is performed by the probabilistic neural network which is trained with sign examples extracted from the real traffic scenes. The obtained results show good accuracy of classification and fast operation time.
机译:本文提出了一种用于识别圆形路标的神经分类器。该分类器属于道路标志识别模块,该模块又是驾驶员辅助系统的一部分。圆形的禁止和义务标志构成一组道路标志中非常重要的组。然而,在这种情况下,检测器不可能确定允许减小搜索空间尺寸的形状的旋转。因此,分类器必须能够正确处理所有可能的仿射变形。为了缓解这个问题,我们建议使用仿射变换仿射组中不变的统计矩作为特征。该分类由概率神经网络执行,该概率神经网络使用从真实交通场景中提取的标志示例进行训练。所得结果表明分类准确度高,运算时间短。

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