Point set pattern matching using the Procrustean metric

1994 ◽  
Author(s):  
Jonathan Phillips
Keyword(s):  
Pattern Matching ◽  
Point Set

scholarly journals Point Pattern Matching Algorithm for Planar Point Sets under Euclidean Transform

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaoyun Wang ◽  
Xianquan Zhang
Keyword(s):  
Pattern Matching ◽  
Complete Graph ◽  
Point Pattern ◽  
Point Sets ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Planar Point ◽  
Point Pattern Matching ◽  
Point Set ◽  
Pattern Matching Algorithm

Point pattern matching is an important topic of computer vision and pattern recognition. In this paper, we propose a point pattern matching algorithm for two planar point sets under Euclidean transform. We view a point set as a complete graph, establish the relation between the point set and the complete graph, and solve the point pattern matching problem by finding congruent complete graphs. Experiments are conducted to show the effectiveness and robustness of the proposed algorithm.

Point Set pattern matching ind-dimensions

Algorithmica ◽  
1995 ◽  
Vol 13 (4) ◽  
pp. 387-404 ◽  
Author(s):  
P. J. de Rezende ◽  
D. T. Lee
Keyword(s):  
Pattern Matching ◽  
Point Set

A GPU-BASED ALGORITHM FOR APPROXIMATELY FINDING THE LARGEST COMMON POINT SET IN THE PLANE UNDER SIMILARITY TRANSFORMATION

2009 ◽  
Vol 09 (02) ◽  
pp. 287-298
Author(s):  
DROR AIGER ◽  
KLARA KEDEM
Keyword(s):  
Pattern Matching ◽  
Similarity Transformation ◽  
Common Point ◽  
Geometric Pattern ◽  
Matching Problem ◽  
Real World Applications ◽  
Geometric Pattern Matching ◽  
Point Set ◽  
Directional Hausdorff Distance ◽  
Transformation Space

We consider the following geometric pattern matching problem: Given two sets of points in the plane, P and Q, and some (arbitrary) δ > 0, find the largest subset B ⊂ P and a similarity transformation T (translation, rotation and scale) such that h(T(B),Q) < δ, where h(.,.) is the directional Hausdorff distance. This problem stems from real world applications, where δ is determined by the practical uncertainty in the position of the points (pixels). We reduce the problem to finding the depth (maximally covered point) of an arrangement of polytopes in transformation space. The depth is the cardinality of B, and the polytopes that cover the deepest point correspond to the points in B. We present an algorithm that approximates the maximum depth with high probability, thus getting a large enough common point set in P and Q. The algorithm is implemented in the GPU framework, thus it is very fast in practice. We present experimental results and compare their runtime with those of an algorithm running on the CPU.

Faster point set pattern matching in 3-D

1998 ◽  
Vol 19 (13) ◽  
pp. 1235-1240 ◽  
Author(s):  
Laurence Boxer
Keyword(s):  
Pattern Matching ◽  
Point Set

Point set pattern matching in 3-D

1996 ◽  
Vol 17 (12) ◽  
pp. 1293-1297 ◽  
Author(s):  
Laurence Boxer
Keyword(s):  
Pattern Matching ◽  
Point Set
Sign in / Sign up

Export Citation Format

Share Document