Junction detection for linear structures based on Hessian, correlation and shape information

Authors:
Ran Su;Changming Sun;Tuan D. Pham
Affiliations:
School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT 2600, Australia and CSIRO Mathematics, Informatics and Statistics, North Ryde, NSW 1670, Australi ...;CSIRO Mathematics, Informatics and Statistics, North Ryde, NSW 1670, Australia;Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan
Venue:
Pattern Recognition
Year:
2012

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Abstract

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.