Paper Info
Title
New Method for Sparse Point-Sets Matching with Underlying Non-Rigidity
Abstract
We propose a novel method for matching two sparse point-sets of identical cardinality with distribution similarity. The point-sets are extracted from two subjects with underlying non-rigidity and non-uniform scaling, one being a model set with point identity and the other representing the observed data. There exists neither a global nor local affine transformations between the point-sets. To establish a one-to-one match, we introduce anew similarity K-dimensional tree which is well adapted and robust to such data. Weconstruct a similarity K-d tree for the model set. Then a corresponding tree of the data set is constructed following the structure information embedded in the model tree. Matching sequences of the two point sets are generated by traversing the identically structured trees. Experimental results based on synthetic data analysis and real data confirm this method is applicable for robust spatial matching of sparse point-sets under non-rigid distortion.
Year
DOI
Venue
2004
10.1109/ICPR.2004.617
ICPR (3)
Keywords
Field
DocType
similarity k-d tree,new method,corresponding tree,observed data,underlying non-rigidity,synthetic data analysis,similarity k-dimensional tree,identically structured tree,model tree,sparse point-sets,synthetic data,k d tree,pattern matching,affine transformation
Rigidity (psychology),Affine transformation,Pattern recognition,Computer science,Cardinality,Synthetic data,Artificial intelligence,3-dimensional matching,Scaling,Distortion,Pattern matching
Conference
ISSN
ISBN
Citations 
1051-4651
0-7695-2128-2
0
PageRank 
References 
Authors
0.34
2
3
Name
Order
Citations
PageRank
Baihua Li117621.71
Qinggang Meng227323.54
H. Holstein3798.21