this paper, a hierarchical method which integrates local analysis (that recovers small details) and global analysis (that appropriately limits possible nonrigid behaviors) is developed to recover dense depth values and nonrigid motion from a sequence of 2D satellite cloud images without any prior knowledge of point correspondences. This problem is challenging not only due to the absence of correspondence information but also due to the lack of depth cues in the 2D cloud images (scaled orthographic projection). In our method, the cloud images are segmented into several small regions and local analysis is performed for each region. A recursive algorithm is proposed to integrate local analysis with appropriate global fluid model constraints, based on which a structure and motion analysis system, SMAS, is developed. We believe that this is the first reported system in estimating dense structure and nonrigid motion under scaled orthographic views using fluid model constraints

The computation of the 2-D motion field from a sequence of images is one of the key tasks of many vision systems. Analysis and interpretation of flow fields is in general a complex task. One of the most interesting problems is to locate critical points in the motion field. The main theme of this thesis is the identification of critical points in motion fields, which have a physical significance for the corresponding application. The discrete Hodge Helmholtz decomposition is a vector decomposition algorithm which is used in this thesis for locating critical points in a motion field. Automatic