Postprocessing and restoration of optical flows

The notion “Optical flow” refers to the apparent motion in the image plane produced by the projection of the real 3D motion onto the 2D image plane. The thesis at hand addresses postprocessing and restoration methods for arbitrarily computed optical flow fields. Many motion estimators have been proposed during the last three decades, but all of them suffer from shortcomings in difficult situations. Hence, it is of utmost importance for any optical flow measurement technique to give a prediction of the quality and reliability of each individual flow vector. Yet, a sound, universally applicable, and statistically motivated confidence measure for optical flow measurements is still missing today. Based on such information, erroneous optical flow fields can be restored or improved by means of inpainting techniques. This thesis introduces three confidence measures, which evaluate the reliability of optical flow vectors. In contrast to previously employed methods, these confidence measures are based on learned motion models and are, thus, statistically motivated, they are independent of the original flow computation method and yield more accurate predictions on the quality of optical flow vectors. The thesis puts a second focus on the restoration of optical flow fields, where it transfers inpainting techniques from the restoration of images to the field of motion recovery. Since the reconstruction process in case of motion fields can use the image sequence as additional source of information, a novel motion inpainting approach is proposed. It combines motion and image information in one functional and, thus, allows to control the orientation of the reconstruction algorithm based on image edges.