Digital Image Correlation (DIC)
Image matching is a discipline of computer vision that is of central importance to a large number of practical applications. To name just a few, image matching is used to solve problems in industrial process control, automatic license plate recognition in parking garages, biological growth phenomena, geological mapping, stereo vision, video compression and autonomous robots for space exploration. Since the applications are so varied, there are a wide variety of approaches and algorithms in use today, many specialized to a given task. For instance, highly specialized algorithms xist to determine motion vectors of small tracer particles used in the study of fluid flows. Digital image correlation is no exception, and algorithms are employed that take the physics of the underlying deformation processes into account. In one regard, however, digital image correlation is somewhat unique. Due to the miniscule motions that are often of interest in engineering applications, the resolution requirements are much higher than for most other applications. To accurately measure the stress-strain curve for many engineering materials, length changes on the order of 10?5 m/m have to be resolved. These requirements have led to the development of many algorithms targeted towards providing high resolution with minimal systematic errors.
This chapter discusses the fundamental problems in image matching with a focus on resolving the motions on the surface of deforming structures. Various fundamental concepts for digital image correlation are presented, and the most commonly used approaches are explored in detail.
KeywordsShape Function Digital Image Correlation Speckle Pattern Stereo Match Digital Image Correlation Algorithm
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