结合几何代数改进的SIFT桥梁裂缝图像拼接算法

王艳; 李宗学; 丁文胜; 沈晓宇

光学技术, 2019, 45 (1): 78, 网络出版: 2019-04-16

结合几何代数改进的SIFT桥梁裂缝图像拼接算法

Improved SIFT algorithm for bridge crack image mosaic combining geometric algebra

论文大纲

王艳 ^1,*李宗学 ¹丁文胜 ²沈晓宇 ¹

作者单位

¹ 上海理工大学　机械工程学院，　上海　200093

² 上海应用技术大学　城市建设与安全工程学院，　上海 201418

裂缝图像几何代数损失函数图像拼接 crack image geometric algebra loss function image stitching

摘要

针对桥梁裂缝图像精度要求高，拼接质量受原图像亮度变化大、噪声干扰严重和对比度低的影响，提出了一种结合几何代数改进的SIFT桥梁裂缝图像的新型拼接算法。对SIFT算法进行了两方面的改进: 一是通过几何代数空间的表示形式提取了待拼接图像的色度图像，克服了SIFT算法中色度信息丢失的不足; 二是改进了SIFT算法对灰度图像建立尺度空间的方法，构建了新的可适用于多光谱图像的高斯滤波和卷积运算，确定了尺度空间。通过几何代数DoG空间检测特征点并进行预匹配。使用改进的RANSAC算法对匹配结果进行修正，完成了图像之间的精确拼接。实验结果表明，所提算法的性能优于SIFT算法，提取的特征点对数量提高了近10%; 拼接过程中未产生位错现象，最终拼接结果满足桥梁裂缝图像的精度要求。

Abstract

Aiming at the high accuracy of bridge crack image and the quality of the mosaic image which is subject to large brightness transformation, serious noise interference and low contrast, a novel SIFT bridge crack image mosaic algorithm combined with geometrical algebra (GA) is presented. The SIFT algorithm is improved in two aspects. The chrominance image of the image to be spliced is extracted through the representation of geometric algebraic space, and the deficiency of chrominance information loss in the SIFT algorithm is overcome. The other hand is that the SIFT algorithm is improved in establish scale space for gray image, and a new Gaussian filtering and convolution operation applicable to multispectral image is constructed, and the scale space is determined. The feature points are detected through the GA-DoG space and the pre-match between images is completed. The improved RANSAC algorithm is used to correct the matching results and complete the precise stitching between images. Experimental results show that the performance of this algorithm is better than SIFT algorithm, and the number of extracted feature point pairs is increased by nearly 10%.There is no dislocation occurred during splicing. The final splicing result satisfies the accuracy requirements of bridge crack images.

参考文献

[1] Lowe D G. Distinctive image features from scale-invariant keypoints ［J］. International Journal of Computer Vision (S0920-5691),2004,60(2):91-100.

[2] 曾峦, 王元钦, 谭久彬. 改进的SIFT特征提取和匹配算法［J］. 光学精密工程,2011,19(6):1391-1397.

Zeng Luan, Wang Yuanqin, Tan Jiubin. Improved algorithm for SIFT feature extraction and matching［J］. Optics and Precision Engineering,2011,19(6):1391-1397.

[3] 陈抒瑢, 李勃, 董蓉, 等. Contourlet-SIFT特征匹配算法［J］. 电子与信息学报,2013(5):1215-1221.

Chen Shurong, Li Bo, Dong Rong, et al. Contourlet-SIFT feature matching algorithm［J］. Journal of Electronics & Information Technology,2013(5): 1215-1221.

[4] 李玉峰, 李广泽, 谷绍湖,等. 基于区域分块与尺度不变特征变换的图像拼接算法［J］. 光学精密工程,2016,24(5):1197-1205.

Li Yufeng, Li Guangze, Gu Shaohu, et al. Image mosaic algorithm based on area blocking and SIFT［J］. Optics and Precision Engineering,2016,24(5):1197-1205.

[5] Lee C, Rhee C E, Lee H J. Complexity reduction by modified scale-space construction in SIFT generation optimized for a mobile GPU［J］. IEEE Transactions on Circuits & Systems for Video Technology,2017,27(10):2246-2259.

[6] 韩超, 方露, 章盛. 一种优化的图像配准算法［J］. 电子测量与仪器学报,2017,31(2):178-184.

Han Chao, Fang Lu, Zhang Sheng. An optimized image registration algorithm［J］. Journal of electronic measurement and instrumentation,2017,31(2):178-184.

[7] Zhang G, Zeng Z, Zhang S, et al. SIFT matching with CNN evidences for particular object retrieval［J］. Neurocomputing,2017,238(238):399-409.

[8] Zhang J, Chen G, Jia Z. An image stitching algorithm based on Histogram matching and SIFT algorithm［J］. International Journal of Pattern Recognition & Artificial Intelligence,2017,31(04):381-395.

[9] 杨志强. 混凝土箱梁裂缝成因分析［D］. 西南交通大学,2005.

Yang Zhiqiang. Analysis for causes of cracking in concrete box girder［D］. Southwest Jiaotong University,2005.

[10] 王强. 基于几何代数的计算机视觉问题研究［D］. 国防科学技术大学,2013.

Wang Qiang. A study of computer vision based on geometric algebra［D］. National University of Defense Technology,2013.

[11] Batard T, Saint-Jean C, Berthier M. A metric approach to nD images edge detection with Clifford Algebras［J］, Journal of Mathematical Imaging and Vision,2009,33 (3):296-312.

[12] Labunets V. Clifford algebras as unified language for image processing and pattern recognition［M］. Computational Noncommutative Algebra and applications. Springer Netherlands,2004:197-225.

[13] Lowe D G. Object recognition from local scale-invariant features［C］∥ IEEE International Conference on Computer Vision　Kerkyra, Greece:IEEE,1999:1150.

[14] 单小军, 唐娉. 图像匹配中误匹配点检测技术综述［J］. 计算机应用研究,2015(9):2561-2565.

Shan Xiaojun, Tang Ping. Review of false matching points detection methods for image matching［J］. Application Research of Computers,2015(9):2561-2565.

[15] Torr P H S, Zisserman A.MLESAC: a new robust estimator with application to estimating image geometry［J］. Computer Vision and Image Understanding,2000,78(1):138-156．

[16] 万国挺, 王俊平, 李锦,等. 图像拼接质量评价方法［J］. 通信学报,2013,34(8):76-81.

Wan Guoting, Wang Junping, Li Jin, et al. Method for quality assessment of image mosaic［J］. Journal on Communications,2013,34(8):76-81.

王艳, 李宗学, 丁文胜, 沈晓宇. 结合几何代数改进的SIFT桥梁裂缝图像拼接算法[J]. 光学技术, 2019, 45(1): 78. WANG Yan, LI Zongxue, DING Wensheng, SHEN Xiaoyu. Improved SIFT algorithm for bridge crack image mosaic combining geometric algebra[J]. Optical Technique, 2019, 45(1): 78.

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