.png){kind=icon}
返回
Unsupervised ridge detection using second order anisotropic Gaussian kernels
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2015.03.024.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Ridge detection
Anisotropic Gaussian Kernel
Multiscale Gaussian kernel
Fungi imagery
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.9K
被引数:
1.7W
机构
引用论文
Grape and wine sensory attributes correlate with pattern-based discrimination of Cabernet Sauvignon wines by a peptidic sensor array.png)
Tetrahedron
TetrahedronIF0
PREAI
分享收藏分享收藏A multimodal registration algorithm of eye fundus images using vessels detection and Hough transform
PREAI
分享收藏Impact of the Timing and Temperature of Malolactic Fermentation on the Aroma Composition and Mouthfeel Properties of Chardonnay WineFoods
OAAI
FoodsIF0
OAAI分享收藏
查看更多内容