Paper Info
Title
A Relationship Between Spline-Based Deformable Models And Weighted Graphs In Non-Rigid Matching
Abstract
Deformable models are central to non-rigid motion analysis, shape matching and non-rigid medical image registration. Spline-based deformations are a very popular class of parameterizations of deformable models and have been heavily used in multiple domains. In a somewhat separate sub-field, weighted graphs are a frequently used object parameterization. Graph matching using weighted graph object parameterizations finds application in a spectrum ranging from rigid pose estimation to deformable object recognition. Here, we demonstrate a hitherto unsuspected relation,ship between spline-based deformable models and weighted graphs. It turns out that spline parameterizations in the kernel representation can be used to construct equivalent weighted graphs. With this connection established, we envision a cross-fertilization between these two seemingly disparate sub-fields of computer vision.
Year
DOI
Venue
2001
10.1109/CVPR.2001.990617
2001 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOL 1, PROCEEDINGS
Keywords
Field
DocType
computer vision,shape,graph matching,image registration,spline,pose estimation,biomedical imaging,spectrum,graph theory,kernel,image analysis,machine learning
Graph theory,Spline (mathematics),Computer vision,Parametrization,Pattern recognition,Computer science,Pose,Matching (graph theory),Artificial intelligence,Motion analysis,Image registration,Cognitive neuroscience of visual object recognition
Conference
ISSN
Citations 
PageRank 
1063-6919
6
0.48
References 
Authors
22
3
Name
Order
Citations
PageRank
A Rangarajan13698367.52
Haili Chui2103458.44
Eric Mjolsness31058140.00