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

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

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 spar...

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Bibliographic Details
Main Authors: Baihua Li, Qinggang Meng, Horst Holstein
Format: Default Article
Published: 2005
Subjects:
Artificial intelligence not elsewhere classified
Other information and computing sciences not elsewhere classified
Dimensional tree
Non-rigid point pattern matching
Non-rigid pose estimation
Robust point pattern correspondence
Motion capture
Artificial Intelligence and Image Processing
Information and Computing Sciences not elsewhere classified
Online Access:https://hdl.handle.net/2134/20267
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https://hdl.handle.net/2134/20267

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