Unsupervised ridge detection using second order anisotropic Gaussian kernels

被引：41

作者：

Lopez-Molina, C. ^{[1
,2
]}

de Ulzurrun, G. Vidal-Diez ^{[2
]}

Baetens, J. M. ^{[2
]}

Van den Bulcke, J. ^{[3
]}

De Baets, B. ^{[2
]}

机构：

[1] Univ Publ Navarra, Dept Automat & Comp, Pamplona 31006, Spain

[2] Univ Ghent, Dept Math Modelling Stat & Bioinformat, KERMIT, B-9000 Ghent, Belgium

[3] Univ Ghent, Dept Forest & Water Management, Lab Wood Technol, B-9000 Ghent, Belgium

来源：

SIGNAL PROCESSING | 2015年 / 116卷

关键词：

Ridge detection; Anisotropic Gaussian Kernel; Multiscale Gaussian kernel; Fungi imagery; EDGE-DETECTION; AUTOMATIC EXTRACTION; BIOIMAGE INFORMATICS; SCALE-SPACE; IMAGES; ALGORITHM; BOUNDARIES; MORPHOLOGY; DIFFUSION; VESSELS;

D O I：

10.1016/j.sigpro.2015.03.024

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We propose the use of the second derivative of Anisotropic Gaussian Kernels for ridge detection. Such kernels, which have proven successful in edge and corner detection, offer interesting advantages over isotropic kernels. In the case of ridge detection, these advantages include the increase of the sensitivity at junctions, as well as an improved characterization of blob-like artefacts. We do not only illustrate these advantages on synthetic images, but also perform a comparison on a new dataset for line detection, which is composed of 100 images of in vitro fungi. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：55 / 67

页数：13

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