Training of templates for object recognition in invertible orientation scores : application to optic nerve head detection in retinal images

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Abstract

A new template matching scheme for the detection of objects on the basis of orientations is proposed. The matching scheme is based on correlations in the domain R2¿S1 of complex valued invertible orientation scores. In invertible orientation scores, a comprehensive overview of how an image is decomposed into local orientations is obtained. The presented approach allows for the efficient detection of orientation patterns in an intuitive and direct way. Furthermore, an energy minimization approach is proposed for the construction of suitable templates. The method is applied to optic nerve head detection in retinal images and extensive testing is done using images from both public and private databases. The method correctly identifies the optic nerve head in 99.7% of 1737 images.
Original languageEnglish
Title of host publicationEnergy Minimization Methods in Computer Vision and Pattern Recognition (10th International Conference, EMMCVPR 2015, Hong Kong, China, January 13-16, 2015. Proceedings)
EditorsX.C. Tai, E. Bae, T.F. Chan, M. Lysaker
Place of PublicationBerlin
PublisherSpringer
Pages464-477
ISBN (Print)978-3-319-14611-9
DOIs
Publication statusPublished - 2015
Event10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2015) - Hong Kong, China
Duration: 13 Jan 201516 Jan 2015
Conference number: 10

Publication series

NameLecture Notes in Computer Science
Volume8932
ISSN (Print)0302-9743

Conference

Conference10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2015)
Abbreviated titleEMMCVPR 2015
CountryChina
CityHong Kong
Period13/01/1516/01/15

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Bekkers, E. J., Duits, R., & Loog, M. (2015). Training of templates for object recognition in invertible orientation scores : application to optic nerve head detection in retinal images. In X. C. Tai, E. Bae, T. F. Chan, & M. Lysaker (Eds.), Energy Minimization Methods in Computer Vision and Pattern Recognition (10th International Conference, EMMCVPR 2015, Hong Kong, China, January 13-16, 2015. Proceedings) (pp. 464-477). (Lecture Notes in Computer Science; Vol. 8932). Berlin: Springer. https://doi.org/10.1007/978-3-319-14612-6_34