A novel computation method for 2D deformation of fish scale based on SURF and N-R optimisation

Guihua Li, P. Ge
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引用次数: 3

Abstract

Fish scales were unique structural materials that served as a form of natural armour and affected the mechanical properties which had important applications in bionics. Digital image correlation (DIC) method was used to determine the mechanical properties, but it took a long time to calculate the uniaxial tensile deformation. In this investigation a DIC optimisation algorithm method based on speeded-up robust features (SURF) and Newton-Raphson (N-R) was conducted on specimens prepared from the scales. First, the SURF algorithm was used to detect the matched points and their coordinate values in the digital images before and after deformation. Then, the initial displacement of the interest point was estimated from the affine transformation fitted to the matched feature points inside the subset area. Last, the zero-mean normalised sum of squared differences (ZNSSD) metric function was optimised by the N-R iterative method, and the optimised displacement value of the interest points would be gained. The numerical translation experiments and simulation results showed that this method improved the search speed and the measurement accuracy effectively. So the deformation of fish scales for axial tension would be calculated by this method.
基于SURF和N-R优化的鱼鳞二维变形计算方法
鱼鳞是一种独特的结构材料,作为一种天然的盔甲,影响着机械性能,在仿生学中有着重要的应用。采用数字图像相关(DIC)方法确定材料的力学性能,但单轴拉伸变形计算时间较长。本研究采用基于加速鲁棒特征(SURF)和牛顿-拉夫森(N-R)的DIC优化算法对从尺度上制备的试件进行优化。首先,利用SURF算法检测变形前后数字图像中的匹配点及其坐标值;然后,通过拟合子集内匹配特征点的仿射变换估计兴趣点的初始位移;最后,采用N-R迭代法对零均值归一化方差和(ZNSSD)度量函数进行优化,得到最优的兴趣点位移值。数值平移实验和仿真结果表明,该方法有效地提高了搜索速度和测量精度。因此,用这种方法可以计算鱼鳞在轴向拉伸下的变形。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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