  Characterization of the deformation of materials across different length
scales has continuously attracted enormous attention from the mechanics and
materials communities. In this study, the possibility of utilizing a computer
vision algorithm to extract deformation information of materials has been
explored, which greatly expands the use of computer vision approaches to
studying mechanics of materials and potentially opens new dialogues between the
two communities. The computer vision algorithm is first developed and tested on
computationally deformed images, before evaluating experimentally collected
images on speckle painted samples before and after deformation. Moreover, a
virtual experiment has also shown the feasibility of mapping surface strain of
a sample based on its natural pattern with significantly improved accuracy
compared to DIC result, which provides new opportunities in experimentation and
computer algorithms to study deformation mechanics of materials. Validation
experiments include evaluating the performance of strain mapping using the
computer vision approach in the uniaxial tensile test and three-point bending
test, compared with extensometer reading and digital image correlation
respectively.
