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

Baihua Li, Q. Meng, Horst Holstein

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
184 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)2391-2399
Number of pages9
JournalPattern Recognition
Volume38
Issue number12
Early online date23 May 2005
DOIs
Publication statusPublished - 01 Dec 2005

Keywords

  • K-dimensional tree
  • non-rigid point-pattern matching
  • non-rigid pose estimation
  • robust point-pattern correspondence
  • motion capture

Fingerprint

Dive into the research topics of 'Similarity K-d tree method for sparse point pattern matching with underlying non-rigidity'. Together they form a unique fingerprint.

Cite this