Non-parametric Models of Distortion in Imaging Systems.
dc.contributor.author | Ranganathan, Pradeep | |
dc.date.accessioned | 2016-06-10T19:30:33Z | |
dc.date.available | NO_RESTRICTION | |
dc.date.available | 2016-06-10T19:30:33Z | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/120690 | |
dc.description.abstract | Traditional radial lens distortion models are based on the physical construction of lenses. However, manufacturing defects and physical shock often cause the actual observed distortion to be different from what can be modeled by the physically motivated models. In this work, we initially propose a Gaussian process radial distortion model as an alternative to the physically motivated models. The non-parametric nature of this model helps implicitly select the right model complexity, whereas for traditional distortion models one must perform explicit model selection to decide the right parametric complexity. Next, we forego the radial distortion assumption and present a completely non-parametric, mathematically motivated distortion model based on locally-weighted homographies. The separation from an underlying physical model allows this model to capture arbitrary sources of distortion. We then apply this fully non-parametric distortion model to a zoom lens, where the distortion complexity can vary across zoom levels and the lens exhibits noticeable non-radial distortion. Through our experiments and evaluation, we show that the proposed models are as accurate as the traditional parametric models at characterizing radial distortion while flexibly capturing non-radial distortion if present in the imaging system. | |
dc.language.iso | en_US | |
dc.subject | camera calibration | |
dc.subject | lens distortion | |
dc.subject | non-parametric model | |
dc.title | Non-parametric Models of Distortion in Imaging Systems. | |
dc.type | Thesis | en_US |
dc.description.thesisdegreename | PhD | |
dc.description.thesisdegreediscipline | Computer Science and Engineering | |
dc.description.thesisdegreegrantor | University of Michigan, Horace H. Rackham School of Graduate Studies | |
dc.contributor.committeemember | Olson, Edwin | |
dc.contributor.committeemember | Scott, Clayton D | |
dc.contributor.committeemember | Lee, Honglak | |
dc.contributor.committeemember | Balzano, Laura Kathryn | |
dc.subject.hlbsecondlevel | Computer Science | |
dc.subject.hlbtoplevel | Engineering | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/120690/1/rpradeep_1.pdf | |
dc.owningcollname | Dissertations and Theses (Ph.D. and Master's) |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.