{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/32693"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/32693","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Video Mosaicking Using Ancillary Data to Facilitate Size Estimation","abstract":"This thesis describes a mosaicking system designed to generate image mosaics that facilitate size estimation of 3-dimensional objects by improving data obtained with a multi-sensor video camera. The multi-sensor camera is equipped with a pulse laser-rangefinder and internally mounted inclinometers that measure instrument orientation about three axes. Using orientation data and video data, mosaics are constructed to reduce orientation data errors by augmenting orientation data with image information. Mosaicking is modeled as a 7-step refinement process: 1) an initial mosaic is constructed using orientation information obtained from the camera's inclinometers; 2) mosaics are refined by using coarse-to-fine processing to minimize an energy metric and, consequently, align overlapping video frames; 3) pair-wise mosaicking errors are detected, and removed, using an energy-based confidence metric; 4) mosaic accuracy is refined via color analysis; 5) mosaic accuracy is refined by estimating an affine transformation to align overlapping frames; 6) affine transformation approximations between overlapping video frames are used to reduce image noise through super-resolution; 7) original orientation data are corrected given the refined orientations of images within the mosaic. The mosaicking system has been tested using objects of known size and orientation accuracy has been improved by 86% for these cases.","abstract_html":"This thesis describes a mosaicking system designed to generate image mosaics that facilitate size estimation of 3-dimensional objects by improving data obtained with a multi-sensor video camera. The multi-sensor camera is equipped with a pulse laser-rangefinder and internally mounted inclinometers that measure instrument orientation about three axes. Using orientation data and video data, mosaics are constructed to reduce orientation data errors by augmenting orientation data with image information. Mosaicking is modeled as a 7-step refinement process: 1) an initial mosaic is constructed using orientation information obtained from the camera's inclinometers; 2) mosaics are refined by using coarse-to-fine processing to minimize an energy metric and, consequently, align overlapping video frames; 3) pair-wise mosaicking errors are detected, and removed, using an energy-based confidence metric; 4) mosaic accuracy is refined via color analysis; 5) mosaic accuracy is refined by estimating an affine transformation to align overlapping frames; 6) affine transformation approximations between overlapping video frames are used to reduce image noise through super-resolution; 7) original orientation data are corrected given the refined orientations of images within the mosaic. The mosaicking system has been tested using objects of known size and orientation accuracy has been improved by 86% for these cases.","abstract_has_math":false,"creators":["Kee, Eric"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Engineering","degree_department":"Electrical and Computer Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Abbott, A. Lynn"],"committee_members":["Cyre, Walling R.","Baker, James M. Jr."],"year":2003,"date_issued":"2003-01-17","date_published":"2003-01-17","updated_at":"2026-07-22T22:19:37Z","subjects":["Computer vision","mosaicking","image processing","mosaicing"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-05132003-221519"],"render_values":[{"text":"etd-05132003-221519","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/32693","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Abbott, A. Lynn"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Cyre, Walling R.","Baker, James M. 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Mosaicking is modeled as a 7-step refinement process: 1) an initial mosaic is constructed using orientation information obtained from the camera's inclinometers; 2) mosaics are refined by using coarse-to-fine processing to minimize an energy metric and, consequently, align overlapping video frames; 3) pair-wise mosaicking errors are detected, and removed, using an energy-based confidence metric; 4) mosaic accuracy is refined via color analysis; 5) mosaic accuracy is refined by estimating an affine transformation to align overlapping frames; 6) affine transformation approximations between overlapping video frames are used to reduce image noise through super-resolution; 7) original orientation data are corrected given the refined orientations of images within the mosaic. The mosaicking system has been tested using objects of known size and orientation accuracy has been improved by 86% for these cases."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["Video Mosaicking Using Ancillary Data to Facilitate Size Estimation"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Abbott, A. Lynn"],"dc:contributor.committeemember":["Cyre, Walling R.","Baker, James M. Jr."],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Kee, Eric"],"dc:date.accessioned":["2014-03-14T20:36:36Z"],"dc:date.available":["2014-03-14T20:36:36Z","2003-06-04"],"dc:date.issued":["2003-01-17"],"dc:description.abstract":["This thesis describes a mosaicking system designed to generate image mosaics that facilitate size estimation of 3-dimensional objects by improving data obtained with a multi-sensor video camera. 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Mosaicking is modeled as a 7-step refinement process: 1) an initial mosaic is constructed using orientation information obtained from the camera's inclinometers; 2) mosaics are refined by using coarse-to-fine processing to minimize an energy metric and, consequently, align overlapping video frames; 3) pair-wise mosaicking errors are detected, and removed, using an energy-based confidence metric; 4) mosaic accuracy is refined via color analysis; 5) mosaic accuracy is refined by estimating an affine transformation to align overlapping frames; 6) affine transformation approximations between overlapping video frames are used to reduce image noise through super-resolution; 7) original orientation data are corrected given the refined orientations of images within the mosaic. 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