Abstract A COMPUTATIONAL FRAMEWORK FOR NEXT-GENERATION INSPECTION IMAGING

Civil inspection images are notoriously difficult to interpret for those who were not present during an inspection, and so they have historically had limited value in on-going condition monitoring applications. Within the last decade the advent of in-expensive digital imaging has compounded this problem by making it possible and common to capture and report numerous images, creating information overload that can easily overwhelm engineers and structure owners. More recently, developments in electronics manufacturing and robotics have made automated structural inspec-tion an emergent technology for civil engineers, which inevitably is leading to even more digital image data that must be handled. This dissertation presents a system-atic approach to capturing, processing, and representing inspection image data such that the inherent value of these increasingly large data sets can be realized. Using a data pipeline combining automated image capture, contextualized 3D visualization, and robust computational imaging and regression techniques, the goal is to allow engineers to view inspection images in their 3D spatial context, while aiding them through enhanced damage detection routines, all while helping to minimize field in