A point-pattern matching method based on maximal subset of one-to-one mapping

2009 ◽  
Author(s):  
Jing Hu ◽  
Tian-Xu Zhang
Keyword(s):  
Pattern Matching ◽  
Point Pattern ◽  
Matching Method ◽  
Point Pattern Matching ◽  
Maximal Subset ◽  
One To One

scholarly journals Approximate one-to-one point pattern matching

2012 ◽  
Vol 15 ◽  
pp. 1-15 ◽  
Author(s):  
Marc Benkert ◽  
Joachim Gudmundsson ◽  
Damian Merrick ◽  
Thomas Wolle
Keyword(s):  
Pattern Matching ◽  
Point Pattern ◽  
Point Pattern Matching ◽  
One To One

Point-pattern matching method using SURF and Shape Context

Optik ◽  
2013 ◽  
Vol 124 (14) ◽  
pp. 1869-1873 ◽  
Author(s):  
Yang Gui ◽  
Ang Su ◽  
Jing Du
Keyword(s):  
Pattern Matching ◽  
Point Pattern ◽  
Shape Context ◽  
Matching Method ◽  
Point Pattern Matching

Combinatorial and Experimental Methods for Approximate Point Pattern Matching

Algorithmica ◽  
2003 ◽  
Vol 38 (1) ◽  
pp. 59-90 ◽  
Author(s):  
Martin Gavrilov ◽  
Piotr Indyk ◽  
Rajeev Motwani ◽  
Suresh Venkatasubramanian
Keyword(s):  
Pattern Matching ◽  
Experimental Methods ◽  
Point Pattern ◽  
Point Pattern Matching

Point pattern matching using convex hull edges

1985 ◽  
Vol SMC-15 (5) ◽  
pp. 631-637 ◽  
Author(s):  
Ardeshir Goshtasby ◽  
George C. Stockman
Keyword(s):  
Convex Hull ◽  
Pattern Matching ◽  
Point Pattern ◽  
Point Pattern Matching

scholarly journals 3D point pattern matching based on spatial geometric flexibility

2010 ◽  
Vol 7 (1) ◽  
pp. 231-246 ◽  
Author(s):  
Xiaopeng Wei ◽  
Xiaoyong Fang ◽  
Qiang Zhang ◽  
Dongsheng Zhou
Keyword(s):  
Pattern Matching ◽  
Spatial Information ◽  
Local Deformation ◽  
Point Pattern ◽  
Similar Distribution ◽  
Point Sets ◽  
Point Pattern Matching ◽  
Rigid Deformation ◽  
Optical Motion ◽  
Optical Motion Capture

We propose a new method for matching two 3D point sets of identical cardinality with global similarity but local non-rigid deformations and distribution errors. This problem arises from marker based optical motion capture (Mocap) systems for facial Mocap data. To establish one-to-one identifications, we introduce a forward 3D point pattern matching (PPM) method based on spatial geometric flexibility, which considers a non-rigid deformation between the two point-sets. First, a model normalization algorithm based on simple rules is presented to normalize the two point-sets into a fixed space. Second, a facial topological structure model is constructed, which is used to preserve spatial information for each FP. Finally, we introduce a Local Deformation Matrix (LDM) to rectify local searching vector to meet the local deformation. Experimental results confirm that this method is applicable for robust 3D point pattern matching of sparse point sets with underlying non-rigid deformation and similar distribution.

A new algorithm for the small-field astrometric point-pattern matching problem

2018 ◽  
Vol 72 (1) ◽  
pp. 55-70 ◽  
Author(s):  
Cláudio P. Santiago ◽  
Carlile Lavor ◽  
Sérgio Assunção Monteiro ◽  
Alberto Kroner-Martins
Keyword(s):  
Pattern Matching ◽  
Point Pattern ◽  
Small Field ◽  
Matching Problem ◽  
Point Pattern Matching

Some Experiments in Point Pattern Matching

1978 ◽  
Author(s):  
Daryl J. Kahl ◽  
Azriel Rosenfeld ◽  
Alan Danker
Keyword(s):  
Pattern Matching ◽  
Point Pattern ◽  
Point Pattern Matching
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