Construction Of3D Statistical Deformable Models For Deformable Objects With Applications In Object Reconstruction And Motion Recognition

With the development of3D scanner and capture devices and computer modeling tools, deformable3D objects have become easy to obtain and have been used in a wide spectrum of fields. How to statistically modeling these deformable3D objects for various applications has become an active research topic.This thesis proposes sophisticated methods to construct3D Statistical Deformable Models (SDMs) for two different forms (i.e.,3D surface form and3D skeleton form) of deformable3D objects for object reconstruction and motion recognition, respectively.For the3D surface form objects, the small sample size problem is frequently encountered when constructing SDM for them, due to their high data dimensions. To address this problem, this thesis constructs piecewise SDM (PSDM) based on divide-and-conquer strategy instead of single global SDM for the objects. To construct a PSDM, two key steps are required:(1) partitioning the surface into multiple components, and (2) assembling the deformed local SDMs to form the final SDM for the object surface. Studying on two different kinds of3D surface data, we propose different techniques for constructing PSDMs for them, respectively. On one hand, the3D medical surface data derived from CT images has a special multi-layer structure that is relatively easier to process. We construct a hierarchical PSDM for it, which consists of a coarse global SDM built on feature points of the surface and a set of local SDMs built on local surface components. The global SDM serves to capture the global variability of the object, and provide a framework for partitioning the surface and also for assembling the local SDMs. The local SDMs serve to capture the local deformation details. On the other hand, the generic3D surface data has no specific structure and is much more difficult to deal with. For the surface partitioning issue, we partition a surface based on the similarity of the surface variability characteristics, and subsequently propose two novel measures for quantifying the variability similarity. For the assembly problem, we employ a technique based on constrained deformation for seamlessly stitching the deformed local SDMs. The PSDMs for the two kinds of3D surface data are both applied to3D object reconstruction. For ensuring the global shape consistency of the entire PSDM, we further propose a multi-level SDM based technique to constrain the deformation of the local SDMs. For the3D skeleton form objects, e.g., the motion capture data, we construct a behavior-specific SDM for each type of the motions in order to capture the common characteristics shared by the motions of that type. The behavior-specific SDM is able to capture and encode all allowable deformation for the type of motions it represents. Taking this property, we propose to classify a new motion based on how well each behavior-specific SDM represents it, i.e., how accurately each behavior-specific SDM can reconstruct the new motion. We show this novel technique is more powerful for3D motion classification compared with the traditional eigen-motions based classification technique. In addition, we also present a novel statistical variation characteristic based measure for quantifying the similarity of3D motions, and apply it to3D motion classification.