Gappy POD and Temporal Correspondence for Lizard Motion Estimation
| dc.contributor.author | Kurdila, Hannah Robertshaw | en |
| dc.contributor.committeechair | Borggaard, Jeffrey T. | en |
| dc.contributor.committeemember | Gugercin, Serkan | en |
| dc.contributor.committeemember | Zietsman, Lizette | en |
| dc.contributor.department | Mathematics | en |
| dc.date.accessioned | 2018-06-21T08:01:12Z | en |
| dc.date.available | 2018-06-21T08:01:12Z | en |
| dc.date.issued | 2018-06-20 | en |
| dc.description.abstract | With the maturity of conventional industrial robots, there has been increasing interest in designing robots that emulate realistic animal motions. This discipline requires careful and systematic investigation of a wide range of animal motions from biped, to quadruped, and even to serpentine motion of centipedes, millipedes, and snakes. Collecting optical motion capture data of such complex animal motions can be complicated for several reasons. Often there is the need to use many high-quality cameras for detailed subject tracking, and self-occlusion, loss of focus, and contrast variations challenge any imaging experiment. The problem of self-occlusion is especially pronounced for animals. In this thesis, we walk through the process of collecting motion capture data of a running lizard. In our collected raw video footage, it is difficult to make temporal correspondences using interpolation methods because of prolonged blurriness, occlusion, or the limited field of vision of our cameras. To work around this, we first make a model data set by making our best guess of the points' locations through these corruptions. Then, we randomly eclipse the data, use Gappy POD to repair the data and then see how closely it resembles the initial set, culminating in a test case where we simulate the actual corruptions we see in the raw video footage. | en |
| dc.description.abstractgeneral | There has been increasing interest over the past few years in designing robots that emulate realistic animal motions. To make these designs as accurate as possible requires thorough analysis of animal motion. This is done by recording video and then converting it into numerical data, which can be analyzed in a rigorous way. But this conversion cannot be made when the raw video footage is ambiguous, for instance, when the footage is blurry, the shot is too dark or too light, the subject (or parts of the subject) are out of view of the camera, etc. In this thesis, we walk through the process of collecting video footage of a lizard running and then converting it into data. Ambiguities in the video footage result in an incomplete translation into numerical data and we use a mathematical technique called the Gappy Proper Orthogonal Decomposition to fill in this incompleteness in an intelligible way. And in the process, we lay your hands on the fundamental drivers of the animal’s motion. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:15350 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/83603 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Gappy proper orthogonal decomposition | en |
| dc.subject | lizard locomotion | en |
| dc.subject | motion capture | en |
| dc.subject | occlusion | en |
| dc.subject | pose estimation | en |
| dc.subject | temporal correspondence | en |
| dc.subject | tracking | en |
| dc.title | Gappy POD and Temporal Correspondence for Lizard Motion Estimation | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Mathematics | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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