Robust Image Corner Detection Through Curvature Scale Space

2003 ◽  
pp. 215-242 ◽  
Author(s):  
Farzin Mokhtarian ◽  
Miroslaw Bober
Keyword(s):  
Scale Space ◽  
Corner Detection ◽  
Curvature Scale Space ◽  
Robust Image

Multi-scale curvature product for robust image corner detection in curvature scale space

2007 ◽  
Vol 28 (5) ◽  
pp. 545-554 ◽  
Author(s):  
Xiaohong Zhang ◽  
Ming Lei ◽  
Dan Yang ◽  
Yuzhu Wang ◽  
Litao Ma
Keyword(s):  
Scale Space ◽  
Corner Detection ◽  
Multi Scale ◽  
Curvature Scale Space ◽  
Robust Image

Robust Image Corner Detection Using Curvature Product in Direct Curvature Scale Space

2010 ◽  
Vol 20-23 ◽  
pp. 725-730 ◽  
Author(s):  
Bao Jiang Zhong ◽  
Chang Li
Keyword(s):  
Gaussian Function ◽  
Computational Cost ◽  
Local Maximum ◽  
Scale Space ◽  
Corner Detection ◽  
Absolute Value ◽  
Multi Scale ◽  
Curvature Scale Space ◽  
Robust Image ◽  
World Image

In this paper we propose an image corner detector based on the direct curvature scale space (DCSS) technique, referred to as the curvature product DCSS (CP-DCSS) corner detector. After the contours of interested objects are extracted from a real-world image, their curvature functions are respectively convolved with the Gaussian function as its standard deviation gradually increases. By measuring the product of the curvature values computed at several given scales, true corners on the contours can be easily detected since false or insignificant corners have been effectively suppressed. A point is declared as a corner when the absolute value of the curvature product exceeds a given threshold and is a local maximum at the mentioned point. CP-DCSS combines the advantages of two recently proposed corner detectors, namely, the DCSS corner detector and the multi-scale curvature product (MSCP) corner detector. Compared to DCSS, CP-DCSS omits a parsing process of the DCSS map, and hence it has a simpler structure. Compared to MSCP, CP-DCSS works equally well, however, at less computational cost.

Separation Algorithm of Traffic Signs Based on Curvature Scale Space Corner Detection

2015 ◽  
Vol 35 (1) ◽  
pp. 0115002 ◽  
Author(s):  
李厚杰 Li Houjie ◽  
邱天爽 Qiu Tianshuang ◽  
宋海玉 Song Haiyu ◽  
王培昌 Wang Peichang ◽  
王鹏杰 Wang Pengjie
Keyword(s):  
Scale Space ◽  
Corner Detection ◽  
Separation Algorithm ◽  
Traffic Signs ◽  
Curvature Scale Space

scholarly journals A Fast Adaptive Corner Detection Based on Curvature Scale Space

2011 ◽  
Vol 14 (5) ◽  
pp. 622-631 ◽  
Author(s):  
Van Hau Nguyen ◽  
Kyung-Haeng Woo ◽  
Won-Ho Choi
Keyword(s):  
Scale Space ◽  
Corner Detection ◽  
Curvature Scale Space

Adaptive Corner Detection Based on Direct Curvature Scale Space

2013 ◽  
Vol 391 ◽  
pp. 488-492
Author(s):  
Bin Liao ◽  
Hui Ying Sun ◽  
Jun Gang Xu
Keyword(s):  
Real Time ◽  
Scale Space ◽  
Corner Detection ◽  
Local Curvature ◽  
Advanced Method ◽  
Multi Scale ◽  
Curvature Scale Space ◽  
Real Time Applications ◽  
Global And Local ◽  
Initial List

Corner detection based on global and local curvature properties is an advanced method for detecting corners in images, which is a fundamental composition of many algorithms. However, we find that it is time-consuming for real-time applications and might detect wrong corners or lose some important corners. To alleviate these problems, we propose an improved curvature product corner detector with dynamic region of support based on Direct Curvature Scale Space (DCSS). Firstly, we use direct curvature scale space to reduce the complexity of computation instead of curvature scale space. Secondly, multi-scale curvature product with certain threshold is used to strengthen the corner detection. Finally, we check the angles of corner candidates in the dynamic region of support in order to eliminate falsely detected corners and use an adaptive curvature threshold to remove round corners from the initial list. The experimental results show that our proposed method improves the performance of corner detection both on accuracy and efficiency, and gain more stable corners at the same time.

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