Similarity K-d tree method for sparse point pattern matching with underlying non-rigidity

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dc.contributor.author Meng, Q.
dc.contributor.author Li, Baihua
dc.contributor.author Holstein, Horst
dc.date.accessioned 2008-12-17T10:27:12Z
dc.date.available 2008-12-17T10:27:12Z
dc.date.issued 2005-12
dc.identifier.citation Meng , Q , Li , B & Holstein , H 2005 , ' Similarity K-d tree method for sparse point pattern matching with underlying non-rigidity ' Pattern Recognition , vol 38 , no. 12 , pp. 2391-2399 . , 10.1016/j.patcog.2005.03.004 en
dc.identifier.other PURE: 97130
dc.identifier.other dspace: 2160/1740
dc.identifier.uri http://hdl.handle.net/2160/1740
dc.description Holstein, Horst, Li, B., Meng, Q., (2005) 'Similarity K-d tree method for sparse point pattern matching with underlying non-rigidity', Pattern Recognition 38(12) pp.2391-2399 RAE2008 en
dc.description.abstract We propose a method for matching non-affinely related sparse model and data point-sets of identical cardinality, similar spatial distribution and orientation. To establish a one-to-one match, we introduce a new similarity K-dimensional tree. We construct the tree for the model set using spatial sparsity priority order. A corresponding tree for the data set is then constructed, following the sparsity information embedded in the model tree. A matching sequence between the two point sets is generated by traversing the identically structured trees. Experiments on synthetic and real data confirm that this method is applicable to robust spatial matching of sparse point-sets under moderate non-rigid distortion and arbitrary scaling, thus contributing to non-rigid point-pattern matching. en
dc.format.extent 9 en
dc.language.iso eng
dc.relation.ispartof Pattern Recognition en
dc.title Similarity K-d tree method for sparse point pattern matching with underlying non-rigidity en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1016/j.patcog.2005.03.004
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Intelligent Robotics Group en
dc.description.status Peer reviewed en


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