基于LK光流和网格运动统计的图像匹配改进算法

doi:10.16180/j.cnki.issn1007-7820.2022.05.001

Abstract

Abstract:

In order to solve the low accuracy and time-consuming problem of AKAZE algorithm in the image matching of glass-encapsulated electrical connectors, improved image matching algorithm based on LK optical flow and grid motion statistics is proposed in this study. First, the AKAZE algorithm is used to extract feature points, and the M-LDB descriptor is used to describe the features. Then, LK optical flow method is used to calculate the matching area for conditional constraints to obtain matching points, and the FLANN algorithm is adopted for feature matching. Finally, the glass-encapsulated electrical connector image is divided into multiple grids, and the numbers and threshold values of correct matching points of the neighborhood of the feature points that are matched by FLANN are calculated to eliminate the wrong matching points. By using public datasets and glass-encapsulated electrical connector data,the performance of the algorithm is verified and analyzed from both real-time and accuracy aspects. The results show that the improved algorithm has a matching accuracy of more than 93% when dealing with image pairs of glass-encapsulated electrical connectors with blur, brightenss and rotation changes, and the time-consuming is within 0.4 s, which proves the effectiveness of the algorithm.

Key words: binocular vision positioning, glass-encapsulated electrical connector, AKAZE algorithm, LK optical flow, feature, matching, FLANN algorithm, grid motion statistics

CLC Number:

TP391

LIU Qunpo,XI Xiulei,YANG Lingxiao. Improved Image Matching Algorithm Based on LK Optical Flow and Grid Motion Statistics[J].Electronic Science and Technology, 2022, 35(5): 1-6.

Figures/Tables 9

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References 19

[1]	Zhang J, Feng Z, Zhang J, et al. An improved randomized local binary features for keypoints recognition[J]. Sensors, 2018, 18(6):1937-1961. doi: 10.3390/s18061937
[2]	Lowe G. SIFT-the scale invariant feature transform[J]. International Journal, 2004(2):91-110.
[3]	Bay H, Tuytelaars T, Van Gool L. Surf: Speeded up robust features[C]. Heidelberg: Proceedings of the Ninth European Conference on Computer Vision, 2006.
[4]	Bay H, Ess A, Liu C, et al. Speeded-up robust features[J]. Computer Vision and Image Understanding, 2008, 110(3):346-359. doi: 10.1016/j.cviu.2007.09.014
[5]	Shao Z, Chen M, Liu C, et al. Feature matching for illumination variation images[J]. Journal of Electronic Imaging, 2015, 24(3):1-11.
[6]	邢凯盛, 凌有铸, 陈孟元, 等. ORB特征匹配的误匹配点剔除算法研究[J]. 电子测量与仪器学报, 2016, 30(8):1255-1262.
	Xing Kaisheng, Ling Youzhu, Chen Mengyuan, et al. Mismatching points elimination algorithm for ORB feature matching[J]. Journal of Electronic Measurement and Instrumentation, 2016, 30(8):1255-1262.
[7]	Lin W Y, Liu S, Jiang N, et al. RepMatch: robust feature matching and pose for reconstructing modern cities[C]. Amsterdam: Proceedings of the European Conference on Computer Vision, 2016.
[8]	Tang P, Peng Y. Exploiting distinctive topological constraint of local feature matching for logo image recognition[J]. Neurocomputing, 2017, 23(6):113-122.
[9]	Bian J W, Lin W Y, Matsushita Y, et al. GMS: grid-based motion statistics for fast, ultra-robust feature correspondence[C]. Honolulu: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017.
[10]	Sedaghat A, Mohammadi N. Uniform competency-based local feature extraction for remote sensing images[J]. Journal of Photogrammetry and Remote Sensing, 2018, 13(5):142-157.
[11]	Prakash C S, Panzade P P, Om H. Detection of copy-move forgery using AKAZE and SIFT key point extraction[J]. Multimedia Tools and Applications, 2019, 78(16):23535-23558. doi: 10.1007/s11042-019-7629-x
[12]	颜焕欢, 张培镇, 王伊侬, 等. 肌骨超声图像特征检测及拼接[J]. 中国图象图形学报, 2020, 25(5):1032-1042.
	Yan Huanhuan, Zhang Peizhen, Wang Yinong, et al. Feature detection algorithm of musculoskeletal ultrasound image and its application of image stitching[J]. Journal of Image and Graphics, 2020, 25(5):1032-1042.
[13]	徐睦浩, 衣禹桥, 李英群, 等. 基于边缘位置与颜色信息的不规则碎纸片拼接方法[J]. 陕西师范大学学报(自然科学版), 2020, 48(6):90-95.
	Xu Muhao, Yi Yuqiao, Li Yingqun, et al. A method of stitching irregular scrapped paper based on edge position and color information[J]. Journal of Shaanxi Normal University(Natural Science Edition), 2020, 48(6):90-95.
[14]	那彦, 廖萌萌. 基于傅里叶梅林变换的SURF算法[J]. 电子科技, 2016, 29(7):55-57.
	Na Yan, Liao Mengmeng.A SURF algorithm based on the Fourier merlin transform[J]. Electronic Science and Technology, 2016, 29(7):55-57.
[15]	严茂森, 陈家琪. 一种多环境适用的交通标志识别模型[J]. 电子科技, 2019, 32(4):68-76.
	Yan Maosen, Chen Jiaqi. A multi-environmental traffic sign recognition model[J]. Electronic Science and Technology, 2019, 32(4):68-76.
[16]	王招娣, 王贤立, 杨数强. 基于颜色和SURF特征的图像匹配算法[J]. 电子科技, 2017, 30(12):67-70.
	Wang Zhaodi, Wang Xianli, Yang Shuqiang. Image matching algorithm based on color and SURF feature[J]. Electronic Science and Technology, 2017, 30(12):67-70.
[17]	于雯越, 安博文, 赵明. 基于光流法与RPCA的红外运动目标检测[J]. 现代计算机, 2018(23):66-71.
	Yu Wenyue, An Bowen, Zhao Ming. Infrared moving target detection based on RPCA and optical flow algorithm[J]. Modern Computer, 2018(23):66-71.
[18]	程俊廷, 郭博洋, 田宽. 改进的LK光流法在SLAM中的应用[J]. 黑龙江科技大学学报, 2019, 29(6):736-740.
	Cheng Junting, Guo Boyang, Tian Kuan. Application of improved LK optical flow method in SLAM[J]. Journal of Heilongjiang University of Science and Technology, 2019, 29(6):736-740.
[19]	Yang X, Huang C, Cheng K T. libLDB:a library for extracting ultrafast and distinctive binary feature description[C]. Orlando: Proceedings of the Twenty-second ACM International Conference on Multimedia, 2014.

图像变化	算法	匹配点对数	正确点对数	CMR/%	时间/s
	SIFT	2 670	1 484	55.58	0.71
旋转变化	SURF	769	319	41.18	1.34
旋转变化	AKAZE	481	277	57.59	0.18
	本文算法	481	449	93.39	0.40
	SIFT	1 671	114	6.82	0.27
光照变化	SURF	1 507	312	20.70	1.31
光照变化	AKAZE	371	176	47.44	0.15
	本文算法	371	349	94.07	0.25
	SIFT	394	160	40.61	0.24
模糊变化	SURF	918	470	51.19	1.27
模糊变化	AKAZE	329	203	61.70	0.18
	本文算法	329	313	95.07	0.20

Improved Image Matching Algorithm Based on LK Optical Flow and Grid Motion Statistics

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