Paper Info
Title
Graph-based shape matching for deformable objects
Abstract
In this paper, we propose a graph-based shape matching method for deformable objects. In our approach, a graph is generated from an over-segmented input image, and the shape matching problem is treated as finding an optimal cycle in the graph. Given a shape template and a graph generated from the input, a product graph is generated to consider every possible correspondence between graph edges and template sub-parts. Because the proposed approach can estimate reasonable correspondences between a target object and a template, it is possible to extract the target object robustly in the presence of shape deformation and background clutter. The experiments on various examples are also presented to verify the performance of proposed method.
Year
DOI
Venue
2011
10.1109/ICIP.2011.6116266
Image Processing
Keywords
Field
DocType
graph theory,image matching,image segmentation,object recognition,shape recognition,background clutter,deformable objects,graph based shape matching,image segmentation,optimal cycle,shape deformation,shape template,Shape matching,deformable shape
Graph theory,Strength of a graph,Object detection,Computer vision,Geometric graph theory,Pattern recognition,Computer science,Image segmentation,Null graph,Graph bandwidth,Artificial intelligence,Cognitive neuroscience of visual object recognition
Conference
ISSN
ISBN
Citations 
1522-4880 E-ISBN : 978-1-4577-1302-6
978-1-4577-1302-6
0
PageRank 
References 
Authors
0.34
6
4
Name
Order
Citations
PageRank
Hanbyul Joo11015.17
Yekeun Jeong2935.51
Olivier Duchenne338210.34
In So Kweon42795207.62