||In this dissertation, a new structured tensor voting method is proposed, which is an alternative approach in Markov random field when the similarity measurement between neighborhood cannot be easily determined. We describe the theoretic analysis from specific situations and focus on applications of the damaged data correction. We propose a robust synthesis algorithm to automatically infer missing texture information from a damaged 2D image by ND tensor voting (N > 3). The same approach is generalized to repair range and 3D data in the presence of occlusion, missing data and noise. Our method can be naturally extended to video repairing, which has the potential for film restoration. Video repairing consists of two parts: static background and moving objects repairing. Given a damaged video as input, our method fills in missing background and estimates foreground movement. In video repairing, we are confronted with the issue of image registration in constructing video mosaics due to intensity inconsistency among images. We propose to solve the problem by intensity voting, and perform image registration with global and intensity alignment. Thus, this thesis represents a significant contribution to the tensor voting methodology, and paves new directions and applications which are impossible with the original framework.