Digital Speckle correlation method improved by hybrid method

Digital Speckle correlation method (DSCM) is a computer-vision-based non-contact, whole-field surface strain measuring method. It is becoming increasingly important as experimental mechanics tool. Its working principle is to compare one digital image of the displaced/deformed surface with the original one using a mathematically well-defined correlation function based on some subset of pixels. In the evolution of DSCM, several methods and techniques have been developed to raise the search speed and precision of DSCM. Typical methods include the coarse-fine search, Newton-Raphson and quasi-Newton method based on optimization theory. However, the coarse-fine search method considers only the rigid body translation displacement, New-Raphson and quasi-Newton is sensitive to the initial values and local extremum. It is possible for Quasi-Newton method to find a local extremum instead of global extremum even impossible to obtain the extremum because bad initial estimate in iterative computation leads to divergence. The genetic algorithm can be used to optimize the subset correlation globally. But it has large computational workload if it is applied to complete all search procedures for whole speckle pattern. Its convergence velocity becomes the slower and slower along with searching near optimum solution. We present an improved DSCM by hybrid method which combines the genetic algorithm quasi-Newton with adjacent point initial estimate. Genetic algorithm is used to obtain the initial estimate for Quasi-Newton iterative method, which be performed only in preliminary search window. Initial estimate for the succedent search subset is obtained by corresponding adjacent point. The simulation experiments have proved the efficiency of the new method.