Motion segmentation is an essential pre-processing task in many computer vision problems. In this dissertation, the motion segmentation problem is studied and analyzed. At first, we establish a framework for the accurate evaluation of the motion field produced by different algorithms. Based on the framework, we introduce a feature tracking algorithm based on RankBoost which automatically prunes bad trajectories. The algorithm is observed to outperform many feature trackers using different measures. Second, we develop three different motion segmentation algorithms. The first algorithm is based on spectral clustering. The affinity matrix is built from the angular information between different trajectories. We also propose a metric to select the best dimension of the lower dimensional space onto which the trajectories are projected. The second algorithm is based on learning. Using training examples, it obtains a ranking function to evaluate and compare a number of motion segmentations generated by different algorithms and pick the best one. The third algorithm is based on energy minimization using the Swendsen-Wang cut algorithm and the simulated annealing. It has a time complexity of $O(N^2)$, comparing to at least $O(N^3)$ for the spectral clustering based algorithms; also it could take generic forms of energy functions. We evaluate all three algorithms as well as several other state-of-the several other state-of-the-art methods on a standard benchmark and show competitive performance. / A Dissertation submitted to the Department of Scientiļ¬c Computing in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Summer Semester, 2013. / June 13, 2013. / Computer Vision, Machine Learning, Motion Segmentation, Object
Tracking / Includes bibliographical references. / Adrian Barbu, Professor Directing Thesis; Anke Meyer-Baese, Professor Co-Directing Thesis; Xiuwen Liu, University Representative; Dennis Slice, Committee Member; Xiaoqiang Wang, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_183697 |
| Contributors | Ding, Liangjing (authoraut), Barbu, Adrian (professor directing thesis), Meyer-Baese, Anke (professor co-directing thesis), Liu, Xiuwen (university representative), Slice, Dennis (committee member), Wang, Xiaoqiang (committee member), Department of Scientific Computing (degree granting department), Florida State University (degree granting institution) |
| Publisher | Florida State University, Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text |
| Format | 1 online resource, computer, application/pdf |
| Rights | This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). The copyright in theses and dissertations completed at Florida State University is held by the students who author them. |
Page generated in 0.002 seconds