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Pattern recognition by affine Legendre moment invariants

机译:仿射Legendre矩不变量的模式识别

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Affine moment invariants are important shape descriptors in pattern recognition and computer vision. Existing affine invariants methods are based on geometric and complex moments. In this paper, we propose a set of affine invariants extracted from Legendre moments. These invariants are derived by the relationship between the Legendre moment of the affine transformed image and that of the original image. The performance of the proposed descriptor is evaluated with a set of binary and gray images. Experimental results show that the proposed method behaves better than existing methods in terms of pattern recognition accuracy.
机译:仿射矩不变性是模式识别和计算机视觉中的重要形状描述符。现有的仿射不变式方法基于几何矩和复杂矩。在本文中,我们提出了从勒让德矩中提取的一组仿射不变量。这些不变量是通过仿射变换后的图像的勒让德矩与原始图像的勒让德矩之间的关系得出的。用一组二进制和灰度图像评估提出的描述符的性能。实验结果表明,该方法在模式识别精度方面表现优于现有方法。

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