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Junction detection for linear structures based on Hessian, correlation and shape information
The University of New South Wales, Canberra, ACT 2600, Australia/ North Ryde, NSW 1670, Australia.
Informatics and Statistics, North Ryde, NSW 1670, Australia.
The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan.
2012 (English)In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 45, no 10, 3695-3706 p.Article in journal (Refereed) PublishedText
Abstract [be]

Junctions have been demonstrated to be important features in many visual tasks such as image registration, matching, and segmentation, as they can provide reliable local information. This paper presents a method for detecting junctions in 2D images with linear structures as well as providing the number of branches and branch orientations. The candidate junction points are selected through a new measurement which combines Hessian information and correlation matrix. Then the locations of the junction centers are refined and the branches of the junctions are found using the intensity information of a stick-shaped window at a number of orientations and the correlation value between the intensity of a local region and a Gaussian-shaped multi-scale stick template. The multi-scale template is used here to detect the structures with various widths. We present the results of our algorithm on images of different types and compare our algorithm with three other methods. The results have shown that the proposed approach can detect junctions more accurately.

Place, publisher, year, edition, pages
2012. Vol. 45, no 10, 3695-3706 p.
Keyword [en]
Junction detection; Linear structure; Correlation matrix; Hessian information; Template
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-127889DOI: 10.1016/j.patcog.2012.04.013OAI: oai:DiVA.org:liu-127889DiVA: diva2:928610
Available from: 2016-05-16 Created: 2016-05-13 Last updated: 2016-05-24

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Pattern Recognition
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