As for the 3D deformable surface recovery, the key challenge arises from the difficulty in estimating a large number of 3D shape parameters from noisy observations. In this thesis, 3D deformable surface tracking is formulated into an unconstrained quadratic problem that can be solved very efficiently by resolving a set of sparse linear equations. Furthermore, the robust progressive finite Newton method developed for nonrigid surface detection is employed to handle the large outliers. / For the appearance-based method, a deformable Lucas-Kanade algorithm is proposed which triangulates the template image into small patches and constrains the deformation through the second order derivatives of the mesh vertices. It is formulated into a sparse regularized least squares problem which is able to reduce the computational cost and the memory requirement. The inverse compositional algorithm is applied to efficiently solve the optimization problem. Furthermore, we present a fusion approach to take advantage of both the appearance information and the local features. / In addition to the methodologies studied and evaluated in computer vision, this thesis also investigates the nonrigid surface recovery in some real-world multimedia applications, such as Near-duplicate image retrieval and detection. In contrast to conventional approaches, the presented technique can recover an explicit mapping between two near-duplicate images with a few deformation parameters and find out the correct correspondences from noisy data effectively. To make the proposed technique applicable to large-scale applications, an effective multilevel ranking scheme is presented that filters out the irrelevant results in a coarse-to-fine manner. To overcome the extremely small training size challenge, a semi-supervised learning method is employed to improve the performance using unlabeled data. Extensive evaluations show that the presented method is clearly effective than conventional approaches. / Recovering deformable surfaces is an interesting and beneficial research problem for computer vision and image analysis. An effective deformable surface recovery technique can be applied in a variety of applications for surface reconstruction, digital entertainment, medical imaging and Augmented Reality. While considerable research efforts have been devoted to deformable surface modeling and fitting, there are only few schemes available to tackle the deformable surface recovery problem efficiently. This thesis proposes a set of methods to effectively solve the 2D nonrigid shape recovery and 3D deformable surface tracking based on a robust progressive optimization scheme. The presented techniques are also applied to a variety of real-world applications. / To tackle the 2D nonrigid shape recovery problem, this thesis first presents a novel progressive finite Newton optimization scheme, which is based on the local feature correspondences. The key of this approach is to formulate the nonrigid shape recovery as an unconstrained quadratic optimization problem which has a closed-form solution for a given set of observations. / Without resorting to an explicit deformable mesh model, the nonrigid surface detection can be treated as a generic regression problem. A novel velocity coherence constraint is imposed on the deformable shape model to regularize the ill-posed optimization problem. To handle the large outliers, a progressive optimization scheme is employed. / Zhu, Jianke. / Adviser: Michael R. Lyu. / Source: Dissertation Abstracts International, Volume: 70-09, Section: B, page: . / Thesis submitted in: December 2008. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 161-175). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_344417 |
| Date | January 2009 |
| Contributors | Zhu, Jianke., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, theses |
| Format | electronic resource, microform, microfiche, 1 online resource (xiv, 175 leaves : ill.) |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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