An online algorithm for matching noisy space curves with statistical error analysis

dc.contributor.advisor Yan-bin Jia Na, Hyuntae
dc.contributor.department Computer Science 2018-08-11T06:10:41.000 2020-06-30T02:41:16Z 2020-06-30T02:41:16Z Sat Jan 01 00:00:00 UTC 2011 2013-06-05 2011-01-01
dc.description.abstract <p>In this thesis, we presents a new algorithm that finds the longest partial match between two space curves. The algorithm iteratively extends an initial matching portion of two curves within some tolerance over the matching quality. Each iteration adjusts the matching transformation (rotation, scale, and translation) to handle noisy data more robustly and to enlarge the matched portion. To control the matching accuracy, a statistical threshold is introduced to stop the iterative extension. Experiment shows that the algorithm has a comparable accuracy to that of the well known ICP algorithm but its efficiency is improved by an order of magnitude. The algorithm has been demonstrated over synthetic and range data. Experiment shows that it adjusts well to noise distributions and performs effectively over curves of complex shapes.</p>
dc.format.mimetype application/pdf
dc.identifier archive/
dc.identifier.articleid 3228
dc.identifier.contextkey 2808426
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/12239
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 19:16:23 UTC 2022
dc.subject.disciplines Computer Sciences
dc.subject.keywords ICP
dc.subject.keywords iterative extension
dc.subject.keywords longest common curve segment
dc.subject.keywords online algorithm
dc.subject.keywords similarity transform
dc.subject.keywords space curve matching
dc.title An online algorithm for matching noisy space curves with statistical error analysis
dc.type article
dc.type.genre thesis
dspace.entity.type Publication
relation.isOrgUnitOfPublication f7be4eb9-d1d0-4081-859b-b15cee251456 thesis Master of Science
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