亚像素渲染
流离失所(心理学)
外推法
趋同(经济学)
算法
数字图像相关
计算机科学
位移场
数学
人工智能
统计
像素
光学
经济增长
热力学
物理
有限元法
经济
心理治疗师
心理学
作者
Hongfan Yang,Sihan Wang,Huanxiong Xia,Jianhua Liu,Aimin Wang,Yong Yang
标识
DOI:10.1088/1361-6501/ac7a06
摘要
Abstract Initial displacement estimation is one of the most critical issues in digital image correlation. A better initial value can greatly improve the convergence rate and accuracy of the algorithms with subpixel accuracy. This paper developed an efficient estimation method to yield high-quality initial displacement fields. This method finds the initial displacement of each subset in a prediction–correction way, in which the displacement of the seed point is found by exhaustive search, but the other subsets are first predicted by an extrapolation scheme and then corrected by a monotonous search strategy. This method was tested by extensive experiments and validated by comparing with the well-known exhaustive search and adaptive rood pattern search methods, and then it was combined with the inverse compositional Gauss–Newton algorithm to perform subpixel-optimization experiments. The results demonstrated excellent features of accuracy, effectiveness, and convergence. Finally, we presented a three-dimensional surface reconstruction experiment using the proposed method, obtaining a geometric accuracy with a relative error of 0.016%.
科研通智能强力驱动
Strongly Powered by AbleSci AI