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基于改进ORB和PROSAC的无人机航拍图像拼接算法

Aerial Image Stitching Algorithm for Unmanned Aerial Vehicles Based on Improved ORB and PROSAC

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摘要

为了满足无人机航拍对图像拼接实时性和稳健性的要求,提出了一种将改进的快速特征点提取和描述(ORB)算法与渐进一致采样(PROSAC)算法相结合的无人机航拍图像拼接算法。首先,利用加速稳健性特征(SURF)算法检测特征点,利用具有旋转特性的二进制稳健基元独立特征(rBRIEF)算法描述特征点,接着利用双向匹配算法和最近邻距离比率策略进行特征点的粗匹配,利用PROSAC算法剔除错误的匹配;然后利用全局单应性变换模型进行图像配准,最后利用渐入渐出图像融合方法进行图像的无缝融合拼接。实验结果表明:该算法在精度和速度上达到很高的平衡,能实现又快又好的图像拼接。

Abstract

To meet the requirements of real-time and robust image stitching of unmanned aerial vehicle (UAV) aerial photography, this paper proposes an aerial image stitching algorithm for UAVs based on an improved fast feature-point extraction and description (ORB) algorithm combined with a progressive sample consensu (PROSAC) algorithm. First, the feature points are detected by the speeded up robust feature (SURF) algorithm and described by the rotation-aware binary robust independent elementary features (rBRIEF) algorithm with rotation characteristics. Next, the bidirectional matching algorithm and nearest-neighbor distance ratio algorithm are used to implement feature point coarse matching; subsequently, the PROSAC algorithm is used to eliminate mismatches. Then, the global homography transformation model is used for image registration. Finally, the gradual-in and gradual-out image blending method is used to seamlessly blend the images. The experimental results indicate that the algorithm achieves excellent balance between accuracy and speed, and realizes fast and good image stitching.

Newport宣传-MKS新实验室计划
补充资料

DOI:10.3788/LOP56.231003

所属栏目:图像处理

基金项目:国家重点研发计划、国网山东省电力公司科技项目;

收稿日期:2019-03-27

修改稿日期:2019-05-27

网络出版日期:2019-12-01

作者单位    点击查看

李振宇:国网山东省电力公司电力科学研究院国网电力系统人工智能联合实验室, 山东 济南 250001山东鲁能智能技术有限公司, 山东 济南 250002
田源:国网山东省电力公司, 山东 济南 250001
陈方杰:上海大学通信与信息工程学院, 上海 200444
韩军:上海大学通信与信息工程学院, 上海 200444

联系人作者:陈方杰(1609951733@qq.com)

备注:国家重点研发计划、国网山东省电力公司科技项目;

【1】Zhang Y H, Jin X, Wang Z J. A new modified panoramic UAV image stitching model based on the GA-SIFT and adaptive threshold method [J]. Memetic Computing. 2017, 9(3): 231-244.

【2】Qu Z, Bu W, Liu L. The algorithm of seamless image mosaic based on A-KAZE features extraction and reducing the inclination of image [J]. IEEJ Transactions on Electrical and Electronic Engineering. 2018, 13(1): 134-146.

【3】Liu S G, Chai Q P. Shape-optimizing and illumination-smoothing image stitching [J]. IEEE Transactions on Multimedia. 2019, 21(3): 690-703.

【4】Han M, Yan K, Qin G S. A mosaic algorithm for UAV aerial image with improved KAZE [J]. Acta Automatica Sinica. 2019, 45(2): 305-314.
韩敏, 闫阔, 秦国帅. 基于改进KAZE的无人机航拍图像拼接算法 [J]. 自动化学报. 2019, 45(2): 305-314.

【5】Brown M, Lowe D G. Automatic panoramic image stitching using invariant features [J]. International Journal of Computer Vision. 2007, 74(1): 59-73.

【6】Lowe D G. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision. 2004, 60(2): 91-110.

【7】Zhang W P, Li X J, Yu J F, et al. Remote sensing image mosaic technology based on SURF algorithm in agriculture [J]. EURASIP Journal on Image and Video Processing. 2018, 2018(1): 85.

【8】Bay H, Ess A, Tuytelaars T, et al. Speeded-up robust features (SURF) [J]. Computer Vision and Image Understanding. 2008, 110(3): 346-359.

【9】Liu T T, Zhang J L. Improved image stitching algorithm based on ORB features by UAV remote sensing [J]. Computer Engineering and Applications. 2018, 54(2): 193-197.
刘婷婷, 张惊雷. 基于ORB特征的无人机遥感图像拼接改进算法 [J]. 计算机工程与应用. 2018, 54(2): 193-197.

【10】Rublee E, Rabaud V, Konolige K, et al. ORB: an efficient alternative to SIFT or SURF . [C]∥2011 International Conference on Computer Vision, November 6-13, 2011, Barcelona, Spain. New York: IEEE. 2011, 2564-2571.

【11】Rosten E, Drummond T. Machine learning for high-speed corner detection [M]. ∥Leonardis A, Bischof H, Pinz A. Computer vision-ECCV 2006. Lecture notes in computer science. Berlin, Heidelberg: Springer. 2006, 3951: 430-443.

【12】Zhao T, Kang H L, Zhang Z P. Fast image mosaic algorithm based on area blocking and BRISK [J]. Laser & Optoelectronics Progress. 2018, 55(3): 031005.
赵婷, 康海林, 张正平. 结合区域分块的快速BRISK图像拼接算法 [J]. 激光与光电子学进展. 2018, 55(3): 031005.

【13】Leutenegger S, Chli M, Siegwart R Y. BRISK: binary robust invariant scalable keypoints . [C]∥2011 International Conference on Computer Vision, November 6-13, 2011, Barcelona, Spain. New York: IEEE. 2011, 2548-2555.

【14】Dong Q, Liu J H, Zhou Q F. Improved SURF algorithm used in image mosaic [J]. Journal of Jilin University(Engineering and Technology Edition). 2017, 47(5): 1644-1652.
董强, 刘晶红, 周前飞. 用于遥感图像拼接的改进SURF算法 [J]. 吉林大学学报(工学版). 2017, 47(5): 1644-1652.

【15】Ren G, Peng D L, Gu Y. Fast image mosaic algorithm based on cylindrical surface mapping [J]. Application Research of Computers. 2017, 34(11): 3472-3476.
任刚, 彭冬亮, 谷雨. 基于圆柱面映射的快速图像拼接算法 [J]. 计算机应用研究. 2017, 34(11): 3472-3476.

【16】Jing J F, Xie J, Li P F. Application of SURB combined with random sample consensus algorithm in shoe uppers matching [J]. Laser & Optoelectronics Progress. 2018, 55(1): 011005.
景军锋, 谢佳, 李鹏飞. 基于SURB结合随机抽样一致算法在鞋面匹配中的应用 [J]. 激光与光电子学进展. 2018, 55(1): 011005.

【17】Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography [J]. Communications of the ACM. 1981, 24(6): 381-395.

【18】Chum O, Matas J, Kittler J. Locally optimized RANSAC [M]. ∥Michaelis B, Krell G. Pattern recognition. DAGM 2003. Lecture notes in computer science. Berlin, Heidelberg: Springer. 2003, 2781: 236-243.

【19】Liu W, Zhao W J, Li C, et al. Detecting small moving target based on the improved ORB feature matching [J]. Opto-Electronic Engineering. 2015, 42(10): 13-20.
刘威, 赵文杰, 李成, 等. 基于改进ORB特征匹配的运动小目标检测 [J]. 光电工程. 2015, 42(10): 13-20.

【20】Chum O, Matas J. Matching with PROSAC: progressive sample consensus . [C]∥2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR''''05), June 20-25, 2005, San Diego, CA, USA. New York: IEEE. 2005, 8588877.

【21】Calonder M, Lepetit V, Strecha C, et al. BRIEF: binary robust independent elementary features [M]. ∥Daniilidis K, Maragos P, Paragios N. Computer vision-ECCV 2010. Lecture notes in computer science. Berlin, Heidelberg: Springer. 2010, 6314: 778-792.

【22】Mikolajczyk K, Tuytelaars T, Schmid C, et al. A comparison of affine region detectors [J]. International Journal of Computer Vision. 2005, 65(1/2): 43-72.

【23】Chen F J, Han J, Wang Z W, et al. Image registration algorithm based on improved GMS and weighted projection transformation [J]. Laser & Optoelectronics Progress. 2018, 55(11): 111006.
陈方杰, 韩军, 王祖武, 等. 基于改进GMS和加权投影变换的图像配准算法 [J]. 激光与光电子学进展. 2018, 55(11): 111006.

引用该论文

Li Zhenyu,Tian Yuan,Chen Fangjie,Han Jun. Aerial Image Stitching Algorithm for Unmanned Aerial Vehicles Based on Improved ORB and PROSAC[J]. Laser & Optoelectronics Progress, 2019, 56(23): 231003

李振宇,田源,陈方杰,韩军. 基于改进ORB和PROSAC的无人机航拍图像拼接算法[J]. 激光与光电子学进展, 2019, 56(23): 231003

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