15 November 2017 High order sum and difference of axial neighborhood algorithm for subpixel edge localization
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Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106054J (2017) https://doi.org/10.1117/12.2297012
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China
Abstract
Affected by the point spread function of optical microscopic imaging system, the edge of microscopic structure and target becomes smooth, at the same time the edge pixel contour distortion is serious because of noise. These factors make positioning precision reduced by using the traditional edge detection algorithm. Thus combining direction information measure and moment invariant theory, the paper puts forward edge detection algorithm of sum and difference of axial neighborhood, and then formulates the high order sum and difference of axial neighborhood to localize sub-pixel edge by using high-order spatial gray moment. Through artificial simulated image the algorithm is test, results show it has stronger antinomies' ability and high positioning accuracy. The algorithm is used in measurement experiment for line width of 1.272μm, the uncertainty is only 0.067μm. This shows that the algorithm reached high accuracy for measurement.
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Jindong Yu, Xianmin Zhang, "High order sum and difference of axial neighborhood algorithm for subpixel edge localization", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106054J (15 November 2017); doi: 10.1117/12.2297012; https://doi.org/10.1117/12.2297012
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