A Self-Adaptive Selection of Subset Size Method in Digital Image Correlation Based on Shannon Entropy

Abstract

Digital image correlation (DIC) is a typical non-contact full-field deformation parameters measurement technique based on image processing technology and numerical computation methods. To obtain the displacements of each point of interrogation in DIC, subsets surrounding the point must be chosen in the reference image and deformed image before correlating. In the existing DIC techniques, the size of subset is always pre-defined by users manually according to their experiences. However, the subset size has proven to be a critical parameter for the accuracy of computed displacements. In the present paper, a self-adaptive selection of subset size method based on Shannon entropy is proposed to overcome the deficiency of existing DIC methods. To verify the effectiveness and accuracy of the proposed algorithm, a numerical translated test is performed on four actual speckle patterns with different entropies, and then another test is performed on four computer-generated speckle patterns with non-uniform displacement field. All the results successfully demonstrate that the proposed algorithm can significantly improve displacement measurement accuracy without reducing too much computational efficiency. Finally, a practical application of the proposed algorithm to micro-tensile of Q235 steel is conducted.

Keywords

• Digital image correlation
• self-adaptive selection
• subset size
• Shannon entropy