Fast corner detection of high contrast image based on mathematical morphology and Curvature Scale Space

2011 ◽  
Author(s):  
Zhenxing Cai ◽  
Zhong Wang ◽  
Lin Li ◽  
Yan Sun
Keyword(s):  
Mathematical Morphology ◽  
Scale Space ◽  
Corner Detection ◽  
High Contrast ◽  
Curvature Scale Space ◽  
Contrast 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

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.

Automatic segmentation algorithm for breast cell image based on multi-scale CNN and CSS corner detection

2020 ◽  
Vol 24 (3) ◽  
pp. 195-203
Author(s):  
Haoyang Tang ◽  
Cong Song ◽  
Meng Qian
Keyword(s):  
Automatic Segmentation ◽  
Scale Space ◽  
Segmentation Algorithm ◽  
Corner Detection ◽  
Adherent Cells ◽  
Multi Scale ◽  
Cell Image ◽  
Curvature Scale Space ◽  
Segmentation Accuracy ◽  
Initial Segmentation

As the shapes of breast cell are diverse and there is adherent between cells, fast and accurate segmentation for breast cell remains a challenging task. In this paper, an automatic segmentation algorithm for breast cell image is proposed, which focuses on the segmentation of adherent cells. First of all, breast cell image enhancement is carried out by the staining regularization. Then, the cells and background are separated by Multi-scale Convolutional Neural Network (CNN) to obtain the initial segmentation results. Finally, the Curvature Scale Space (CSS) corner detection is used to segment adherent cells. Experimental results show that the proposed algorithm can achieve 93.01% accuracy, 93.93% sensitivity and 95.69% specificity. Compared with other segmentation algorithms of breast cell, the proposed algorithm can not only solve the difficulty of segmenting adherent cells, but also improve the segmentation accuracy of adherent cells.

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