基于改进剪切波的角点检测
陕西师范大学, 物理学与信息技术学院, 陕西西安 710119
| 摘 要： | 图像角点在图像处理与分析、机器视觉等领域具有重要理论意义与应用价值，然而，现有的多尺度分析角点检测器未能充分利用多尺度和多方向的结构信息，这影响了它们的检测精度和对角点的定位能力。基于剪切波变换的基本原理，分析其在图像多尺度、多方向分解的局限性，将尺度函数改进为较为平坦下降沿的巴特沃斯型，之后针对辅助函数改进得到新的非零可积函数，进而提出新的基于改进剪切波的多尺度、多方向图像分解方法。实验结果表明该方法不仅在图像多尺度分解后得到的小尺度的纹理效果得到部分提升，解决了之前剪切波小尺度局部图像与原图像差异性大、纹理杂乱的问题，而且图像重构效果亦有所改进且具有较强的鲁棒性。在图像角点检测领域利用该算法可充分利用多尺度和多方向的结构信息进而提升图像角点检测的定位精度，降低错检、漏检率，而且具有较好的鲁棒性。 |
| 关 键 词： | 剪切波; 多尺度图像分解; 角点检测 |
| DOI： | 10.57237/j.cst.2025.03.002 |
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收稿日期: 2025-06-08
接受日期: 2025-08-08
在线出版日期: 2025-09-09
Corner Detection Based on Improved Shearlet Transform
College of Physics and Information Engineering, Shaanxi Normal University, Xi'an 710119, China
| Abstract: | Image corners have important theoretical significance and application value in the fields of image processing and analysis, machine vision, etc., however, the existing multi-scale analysis corner detectors fail to make full use of the multi-scale and multi-direction structural information, which affects their detection accuracy and diagonal positioning ability. Based on the basic principle of shear wave transform, the limitations of multi-scale and multi-direction image decomposition are analyzed, and the scale function is improved to the Butterworth type with a relatively flat falling edge, and then a new non-zero integrable function is obtained for the auxiliary function, and then a new multi-scale and multi-directional image decomposition method based on improved shear wave is proposed. Experimental results show that the proposed method not only partially improves the small-scale texture effect obtained after multi-scale image decomposition, but also solves the problem of large difference and messy texture between the previous small-scale local image and the original image of the clipping wave, and the image reconstruction effect is also improved and has strong robustness. In the field of image corner detection, the algorithm can make full use of multi-scale and multi-direction structural information to improve the positioning accuracy of image corner detection, reduce the error detection and missed detection rate, and has good robustness. |
| Keywords: | Shearlet; Multiscale Image Decomposition; Corner Detection |
参考文献
| [1] | Wang M, Sun C, Sowmya A. Complex shearlets and rotary phase congruence tensor for corner detection [J]. Pattern Recognition, 2022, 128(8): 108606-108606. |
| [2] | Raas A, Sg A. Comparative Analysis of Eight Direction Sobel Edge Detection Algorithm for Brain Tumor MRI Images [J]. Procedia Computer Science, 2022, 201(1): 487-494. |
| [3] | 张阳, 刘缠牢, 卢伟家, 等. 基于 LoG 算子的双滤波边缘检测算法 [J]. 电子测量技术, 2019(4): 95-98. |
| [4] | Aishwarya N, Thangammal C B. Visible and infrared image fusion using DTCWT and adaptive combined clustered dictionary [J]. Infrared Physics and Technology, 2018, 93: 300-309. |
| [5] | Do M N, Vetterli M. The contourlet transform: An efficient directional multi-resolution image representation [J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106. |
| [6] | Duan C, Wang S, Huang Q, et al. Feature level MRI fusion based on 3D dual tree compactly supported Shearlet transform [J]. Journal of Visual Communication and Image Representation, 2019, 60(2): 319-327. |
| [7] | Duval-Poo M A, Noceti N, Odone F, et al. Scale invariant and noise robust interest points with shearlets [J]. IEEE Transactions on Image Processing, 2017, 26(6): 2853-2867. |
| [8] | Duval-Poo M A, Odone F, Vito E D. Enhancing signal discontinuities with shearlets: An application to corner detection [C] // International Conference on Image Analysis and Processing, 2015: 108-118. |
| [9] | Zhang W C, Sun C. Corner detection using multi-directional structure tensor with multiple scales [J]. International Journal of Computer Vision, 2020, 128(2): 438-459. |
| [10] | 田子怡, 李云红. 基于多尺度 Gabor 滤波器的角点检测 [J]. 国外电子测量技术, 2016, 35(7): 75-79. |
| [11] | Teng S W, Najmus Sadat R M, Lu G. Effective and efficient contour-based corner detectors [J]. Pattern Recognition, 2015, 48(7): 2185-2197. |
| [12] | 施雅琦. 基于Log-Gabor滤波器的角点检测算法及其应用 [D]. 西安: 西安电子科技大学, 2021: 52-60. |
| [13] | Wang M, Zhang W, Sun C, et al. Corner detection based on shearlet transform and multi-directional structure tensor [J]. Pattern Recognition, 2020, 103: 107299. |
| [14] | Leutenegger S, Chli M, et al. BRISK: Binary robust invariant scalable keypoints [C] // IEEE International Conference on Computer Vision, 2011: 2548-2555. |
| [15] | Chen Y S. Classifying credit ratings for Asian banks using integrating feature selection and the CPDA-based rough sets approach [J]. Knowledge-Based Systems, 2012, 26(1): 259-270. |
| [16] | Bay H, Ess A, Tuytelaars T, et al. Speeded-up robust features (SURF) [J]. Computer Vision & Image Understanding, 2008, 110(3): 346-359. |
| [17] | Kim S. Robust corner detection by image-based direct curvature field estimation for mobile robot navigation [J]. International Journal of Advanced Robotic Systems, 2012, 9(5): 700-709. |
| [18] | Zhang W C, Shui P L. Contour-based corner detection via angle difference of principal directions of anisotropic Gaussian directional derivatives [J]. Pattern Recognition, 2015, 48(9): 2785-2797. |
| [19] | Xia G S, Delon J, Gousseau Y. Accurate junction detection and characterization in natural images [J]. International Journal of Computer Vision, 2014, 106(1): 31-56. |
