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Image registration using an adaptive LMS algorithm

机译:使用Adaptive LMS算法进行图像注册

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The adaptive LMS registration algorithm has shown the potential to be as accurate as the Fourier shift theorem (FST) registration method for small translational displacements, however with larger displacements the accuracy of the LMS method can degrade relative to the FST approach. The LMS algorithm also appears to be less robust to noise than the FST. The LMS method was over twice as fast as the FST method for the experiments described with large unknown rotations. Further speed increases would be expected using a variable step-size algorithm and automatic detection of convergence. A variable step-size algorithm may also reduce the variations in performance between images of different structure. A cursory investigation of the effect of grey-scale stretching on registration error found no benefits when applied uniformly to the whole image.
机译:自适应LMS登记算法示出了与小型平移位移的傅里叶移位定理(FST)登记方法一样准确,但是随着较大的位移,LMS方法的精度可以相对于FST方法降低。 LMS算法也似乎对噪声较低而不是FST。 LMS方法超过了具有大未知旋转的实验的FST方法的两倍。使用可变的步长算法和自动检测会收敛,预期进一步的速度增加。可变步长算法还可以减少不同结构图像之间的性能的变化。在均匀图像施加到整个图像时,对注册误差施加灰度伸展效果的施用调查没有任何好处。

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