• A robust global and local mixt...

Treffer: A robust global and local mixture distance based non-rigid point set registration

Title:
A robust global and local mixture distance based non-rigid point set registration
Authors:
YANG YANG, SIM HENG ONG, KELVIN WENG CHIONG FOONG
Source:
Pattern recognition. 48(1):156-173
Publisher Information:
Kidlington: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 42 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Alignement, Alignment, Alineamiento, Approximation spline, Spline approximation, Aproximación esplín, Distance, Distancia, Déformation mécanique, Mechanical deformation, Deformación mecánica, Estimation ponctuelle, Point estimation, Estimación puntual, Imagerie médicale, Medical imagery, Imaginería médica, Minimisation coût, Cost minimization, Minimización costo, Mise à jour, Updating, Actualización, Plaque mince, Thin plate, Placa delgada, Recalage image, Image registration, Registro imagen, Recuit, Annealing, Recocido, Vision ordinateur, Computer vision, Visión ordenador, Correspondence estimation, Global and local mixture distance, Multi-feature based framework, Non-rigid point set registration, Transformation updating
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
School of Information Science and Technology, Yunnan Normal University, Kunming 650092, Yunnan, China
The Engineering Research Center of GIS Technology in Western China of Ministry of Education of China, Yunnan Normal University, Kunming 650092, Yunnan, China
Key Laboratory of Education Informatization for Nationalities of Ministry of Education of China, Yunnan Normal University, Kunming 650092, Yunnan, China
NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore 117456, Singapore
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
Department of Bioengineering, National University of Singapore, Singapore 117576, Singapore
Faculty of Dentistry, National University of Singapore, Singapore 119083, Singapore
ISSN:
0031-3203
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28858351
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.28858351
Database:
PASCAL Archive

Weitere Informationen

We present a robust global and local mixture distance (GLMD) based non-rigid point set registration method which consists of an alternating two-step process: correspondence estimation and transformation updating. We first define two distance features for measuring global and local structural differences between two point sets, respectively. The two distances are then combined to form a GLMD based cost matrix which provides a flexible way to estimate correspondences by minimizing global or local structural differences using a linear assignment solution. To improve the correspondence estimation and enhance the interaction between the two steps, an annealing scheme is designed to gradually change the cost minimization from local to global and the thin plate spline transformation from rigid to non-rigid during registration. We test the performance of our method in contour registration, sequence images and real images, and compare with six state-of-the-art methods where our method shows the best alignments in most scenarios.