dc.contributor.advisor | Dahyot, Rozenne | |
dc.contributor.author | Ruttle, Jonathan | |
dc.date.accessioned | 2016-11-07T16:30:13Z | |
dc.date.available | 2016-11-07T16:30:13Z | |
dc.date.issued | 2012 | |
dc.identifier.citation | Jonathan Ruttle, 'Statistical framework for multi sensor fusion and 3D reconstruction', [thesis], Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2012, pp 326 | |
dc.identifier.other | THESIS 10044 | |
dc.identifier.uri | http://hdl.handle.net/2262/77655 | |
dc.description.abstract | Multi-view 3D reconstruction is an area of computer vision where multiple images are
taken of an object and information in those images is used to generate a 3D model
describing the shape and size of that object. The ability to automatically generate 3D
models of objects has many uses from content creation for games to object recognition
and is a first step in many other computer vision tasks like markerless motion capture.
In this thesis a new framework to achieve 3D reconstruction is presented. This
framework is based on the generalised Radon transform and is linked to kernel density
estimation. A new smooth differentiable function is defined that can be optimised using
gradient ascent algorithms. The framework is applied to two applications; firstly to
computing the visual hull, a 3D reconstruction from multiple silhouettes and secondly,
to generate a 3D reconstruction from depth information. The framework is capable of
overcoming the considerable noise present in depth data to generate an accurate 3D
reconstruction.
The framework is extended to optimise camera alignment parameters in a multicamera
system. Existing techniques for calculating camera parameters can be prone
to error. This extension optimises these initial estimates of the camera parameters to
facilitate accurate 3D reconstructions in real environments.
Finally two data-sets were generated and captured to test and evaluate all the
algorithms developed. | |
dc.format | 1 volume | |
dc.language.iso | en | |
dc.publisher | Trinity College (Dublin, Ireland). School of Computer Science & Statistics | |
dc.relation.isversionof | http://stella.catalogue.tcd.ie/iii/encore/record/C__Rb15350147 | |
dc.subject | Statistics, Ph.D. | |
dc.subject | Ph.D. Trinity College Dublin | |
dc.title | Statistical framework for multi sensor fusion and 3D reconstruction | |
dc.type | thesis | |
dc.type.supercollection | refereed_publications | |
dc.type.supercollection | thesis_dissertations | |
dc.type.qualificationlevel | Doctoral | |
dc.type.qualificationname | Doctor of Philosophy (Ph.D.) | |
dc.rights.ecaccessrights | openAccess | |
dc.format.extentpagination | pp 326 | |
dc.description.note | TARA (Trinity's Access to Research Archive) has a robust takedown policy. Please contact us if you have any concerns: rssadmin@tcd.ie | |