Direct curvature scale space: Theory and corner detection
Direct curvature scale space: Theory and corner detection
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DOI：
10.1109/tpami.2007.50
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发表时间：
2007-03-01
影响因子：
23.6
通讯作者：
Liao, Wenhe
中科院分区：
文献类型：
--
作者：
Zhong, Baojiang;Liao, Wenhe
曲率尺度空间（CSS）技术被认为是图像处理和计算机视觉领域的一种现代工具。直接曲率尺度空间（Direct Curvature Scale Space， DCSS）是由平面曲线的曲率与高斯核直接卷积得到的空间。本文从理论上分析了DCSS在平面曲线角点检测中的应用。研究了孤立的单角点和双角点模型的尺度空间行为，并确定了一些模型属性，使我们能够将DCSS图像转换为树形组织，从而可以在多尺度意义上检测角点。为了克服DCSS对噪声的敏感性，提出了一种混合应用的策略。
The Curvature Scale Space (CSS) technique is considered to be a modern tool in image processing and computer vision. Direct Curvature Scale Space (DCSS) is defined as the CSS that results from convolving the curvature of a planar curve with a Gaussian kernel directly. In this paper we present a theoretical analysis of DCSS in detecting corners on planar curves. The scale space behavior of isolated single and double corner models is investigated and a number of model properties are specified which enable us to transform a DCSS image into a tree organization and, so that corners can be detected in a multiscale sense. To overcome the sensitivity of DCSS to noise, a hybrid strategy to apply CSS and DCSS is suggested.