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Two Step Template Matching Method with Correlation Coefficient and Genetic Algorithm

机译:相关系数和遗传算法的两步模板匹配方法

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This paper presents a rotation invariant template matching method based on two step matching process, cross correlation and genetic algorithm. In order to improve the matching performance, the traditional normalized correlation coefficient method is combined with genetic algorithm. Normalized correlation coefficient method computes probable local position of the template in the scene image. And genetic algorithm computes global position and rotation of the template in the scene image. The experimental results show that this algorithm has good rotate invariance, and high precision property.
机译:本文提出了一种基于两步匹配,互相关和遗传算法的旋转不变模板匹配方法。为了提高匹配性能,将传统的归一化相关系数法与遗传算法相结合。归一化相关系数法计算场景图像中模板的可能局部位置。遗传算法可以计算出场景图像中模板的整体位置和旋转度。实验结果表明,该算法具有良好的旋转不变性和较高的精度。

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