Shape Representation based on Integral Kernels: Application to Image Matching and Segmentation

Byung-Woo Hong 1 Emmanuel Prados 1, 2 Stefano Soatto 1 Luminita Vese 1
1 UCLA-CS - Computer Science Department [UCLA]
2 MOVI - Modeling, localization, recognition and interpretation in computer vision
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : This paper presents a shape representation and a variational framework for the construction of diffeomorphisms that establish ?meaningful? correspondences between images, in that they preserve the local geometry of singularities such as region boundaries. At the same time, the shape representation allows enforcing shape information locally in determining such region boundaries. Our representation is based on a kernel descriptor that characterizes local shape. This shape descriptor is robust to noise and forms a scale-space in which an appropriate scale can be chosen depending on the size of features of interest in the scene. In order to preserve local shape during the matching procedure, we introduce a novel constraint to traditional energybased approaches to estimate diffeomorphic deformations, and enforce it in a variational framework.
Document type :
Conference papers
Complete list of metadatas

Cited literature [40 references]  Display  Hide  Download

Hong-Prados-etal-cvpr2006.jpg
Hong-Prados-etal-cvpr2006.jpg
https://hal.inria.fr/inria-00377421
Contributor : Emmanuel Prados <>
Submitted on : Tuesday, April 21, 2009 - 5:07:09 PM
Last modification on : Wednesday, August 7, 2019 - 12:14:40 PM
Long-term archiving on: Thursday, June 10, 2010 - 7:11:52 PM

Files

Hong-Prados-etal-CVPR_2006.pdf
Files produced by the author(s)

Identifiers

Collections

CNRS | INRIA | IMAG | UGA

Citation

Byung-Woo Hong, Emmanuel Prados, Stefano Soatto, Luminita Vese. Shape Representation based on Integral Kernels: Application to Image Matching and Segmentation. IEEE Conference on Computer Vision and Pattern Recognition, Jun 2006, New York, United States. pp.833- 840, ⟨10.1109/CVPR.2006.277⟩. ⟨inria-00377421⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

307

Files downloads

345

 

 

Contact

archive-ouverte@inria.fr