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Digital image correlation and tracking is an optical method that employs tracking and image registration techniques for accurate 2D and 3D measurements of changes in images. This is often used to measure deformation (engineering), displacement, strain, and optical flow, but it is widely applied in many areas of science and engineering. One very common application is for measuring the motion of an optical mouse. ==Overview== Digital image correlation (DIC) techniques have been increasing in popularity, especially in micro- and nano-scale mechanical testing applications due to its relative ease of implementation and use. Advances in computer technology and digital cameras have been the enabling technologies for this method and while white-light optics has been the predominant approach, DIC can be and has been extended to almost any imaging technology. The concept of using cross-correlation to measure shifts in datasets has been known for a long time, and it was applied to digital images at since the early 1970s.〔P. E. Anuta, "(Spatial registration of multispectral and multitemporal digital imagery using fast Fourier transform techniques )," IEEE Trans. Geosci. Electron., vol. GE-8, pp. 353-368, Oct. 1970〕〔T.J. Keating, P.R. Wolf, and F.L. Scarpace,"An Improved Method of Digital Image Correlation," ''Photogrammetric Engineering and Remote Sensing'' 41(8):993-1002,(1975)〕 The present day applications are almost innumerable and include image analysis, image compression, velocimetry, and strain estimation. Much early work in DIC in the field of mechanics was led by researchers at the University of South Carolina in the early 1980s〔T.C. Chu, W.F. Ranson, M.A. Sutton, W.H. Peters, Exp Mech 25 (1985) 232.〕〔H.A. Bruck, S.R. McNeill, M.A. Sutton, W.H. Peters III, Exp Mech 29 (1989) 261.〕〔W.H. Peters, W.F. Ranson, Opt Eng 21 (1982) 427.〕 and has been optimized and improved in recent years.〔E.g. M.A. Sutton, J.-J. Orteu, H. W. Schreier, Book - Image Correlation for Shape, Motion and Deformation Measurements, Hardcover ISBN 978-0-387-78746-6.〕 Commonly, DIC relies on the maximization of a correlation coefficient that is determined by examining pixel intensity array subsets on two or more corresponding images. However, for subpixel interpolation of the shift, there are other methods that do not simply maximize the correlation coefficient, as is common in phase correlation. An iterative approach can also be used to maximize the interpolated correlation coefficient by using nonlinear optimization techniques. The nonlinear optimization approach tends to be conceptually simpler, but as with most nonlinear optimization techniques, it is quite slow, and the problem can sometimes be reduced to a much faster and more stable linear optimization in phase space. The cross correlation coefficient is defined as : Here F(xi, yj) is the pixel intensity or the gray scale value at a point (xi, yj) in the original image. G(xi *, yj *) is the gray scale value at a point (xi *, yj *) in the translated image. and are mean values of the intensity matrices F and G, respectively. However, in practical applications, the correlation coefficient array is usually computed using Fourier transform methods, since the fast Fourier transform is a much faster method than directly computing the correlation. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Digital image correlation」の詳細全文を読む スポンサード リンク
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