留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于自适应深度约束的水下双目图像特征匹配

田嘉禾 王宁 陈廷凯 李春艳 陈帅

田嘉禾, 王宁, 陈廷凯, 等. 基于自适应深度约束的水下双目图像特征匹配[J]. 中国舰船研究, 2021, 16(6): 124–131 doi: 10.19693/j.issn.1673-3185.02197
引用本文: 田嘉禾, 王宁, 陈廷凯, 等. 基于自适应深度约束的水下双目图像特征匹配[J]. 中国舰船研究, 2021, 16(6): 124–131 doi: 10.19693/j.issn.1673-3185.02197
TIAN J H, WANG N, CHEN T K, et al. Adaptive depth constraint-based underwater binocular image feature matching[J]. Chinese Journal of Ship Research, 2021, 16(6): 124–131 doi: 10.19693/j.issn.1673-3185.02197
Citation: TIAN J H, WANG N, CHEN T K, et al. Adaptive depth constraint-based underwater binocular image feature matching[J]. Chinese Journal of Ship Research, 2021, 16(6): 124–131 doi: 10.19693/j.issn.1673-3185.02197

基于自适应深度约束的水下双目图像特征匹配

doi: 10.19693/j.issn.1673-3185.02197
基金项目: 辽宁省“兴辽英才计划”资助项目(XLYC1807013);装备预研重点实验室基金资助项目(6142215200106);基础加强计划重点基础研究项目资助(2020-JCJQ-ZD-153);青年科学家培育基金资助项目(79000006)
详细信息
    作者简介:

    田嘉禾,男,1996年生,硕士。研究方向:双目视觉三维重建。E-mail:tianj_h@163.com

    王宁,男,1983年生,博士,教授,博士生导师。研究方向:无人系统建模与控制,自适应智能控制,机器学习,非线性控制。E-mail:n.wang.dmu.cn@gmail.com

    陈廷凯,男,1993年生,博士生。研究方向:目标识别与检测。E-mail:tingkai.chen@aliyun.com

    通信作者:

    王宁

  • 中图分类号: U662.9

Adaptive depth constraint-based underwater binocular image feature matching

知识共享许可协议
基于自适应深度约束的水下双目图像特征匹配田嘉禾,等创作,采用知识共享署名4.0国际许可协议进行许可。
  • 摘要:   目的  针对水下双目图像特征点稀疏、极线约束模型失效等难题,提出一种基于自适应深度约束的水下图像特征匹配(ADC-UFM)算法。  方法  结合FAST算子与SIFT描述子,提高图像匹配精度;提出基于水下折射因子的特征匹配约束模型(MCM),有效剔除误匹配点;提出自适应阈值选取(ATC)方法,最大限度地保留复杂水下环境下的图像特征信息。  结果  实验结果显示,ADC-UFM算法优于现有的SIFT,SURF和UCC-SIFT等典型方法,匹配准确率可达85.2%,满足实时匹配需求。  结论  研究成果可为基于双目视觉系统的水下三维重建提供关键技术支撑。
  • 图  1  FAST特征检测原理图

    Figure  1.  Schematic diagram of FAST feature detection

    图  2  关键点主方向分配原理图

    Figure  2.  Schematic diagram of main direction assignment of key points

    图  3  水下反投影模型

    Figure  3.  Underwater back projection model

    图  4  水下图像匹配流程图

    Figure  4.  Flow chart of underwater image matching

    图  5  水下数据采集平台

    Figure  5.  Underwater data acquisition platform

    图  6  不同数据集匹配结果

    Figure  6.  Matching results under different data sets

    图  7  不同算法匹配准确率对比图

    Figure  7.  Comparison of matching accuracy of different algorithms

    表  1  双目相机内部参数

    Table  1.   Internal parameters of binocular camera

    参数左相机右相机
    (fx, fy)(1 201.30, 1 200.86)(1 211.81, 1 207.01)
    (u0, v0)(319.24, 255.25)(322.34, 316.32)
    K(0.28, −0.74, 0.01, 0.03, 0.0)(−0.10, −0.06, 0.02, 0.01, 0.00)
    下载: 导出CSV

    表  2  实验结果数据对比

    Table  2.   Comparison of experimental data

    数据来源算法匹配对数正确率/%匹配时间/s
    采集的数据暗光SIFT5384.90.108
    SURF6484.30.060
    UCC-SIFT2684.61.930
    ADC-UFM5286.50.721
    旋转SIFT9882.70.112
    SURF10383.40.062
    UCC-SIFT5284.61.963
    ADC-UFM7384.90.730
    浑浊SIFT3479.40.093
    SURF7078.50.058
    UCC-SIFT2986.21.884
    ADC-UFM4082.50.710
    公开数据暗光SIFT6281.30.098
    SURF8082.50.060
    UCC-SIFT4383.71.901
    ADC-UFM6085.00.712
    旋转SIFT12184.20.293
    SURF10783.10.061
    UCC-SIFT6285.41.970
    ADC-UFM9085.60.732
    浑浊SIFT17284.30.300
    SURF19583.50.064
    UCC-SIFT9885.71.991
    ADC-UFM12086.70.753
    下载: 导出CSV
  • [1] KAWAI R, YAMASHITA A, KANEKO T. Three-dimensional measurement of objects in water by using space encoding method[C]//Proceedings of 2009 IEEE International Conference on Robotics and Automation. Kobe, Japan: IEEE, 2009: 2830−2835.
    [2] BROWN M Z, BURSCHKA D, HAGER G D. Advances in computational stereo[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(8): 993–1008. doi: 10.1109/TPAMI.2003.1217603
    [3] LOWE D G. Distinctive image features from scale-invariant keypoint[J]. International Journal of Computer Vision, 2004, 60(2): 91–110. doi: 10.1023/B:VISI.0000029664.99615.94
    [4] BAY H, ESS A, TUYTELAARS T, et al. Speeded-up robust features (SURF)[J]. Computer Vision and Image Understanding, 2008, 110(3): 346–359. doi: 10.1016/j.cviu.2007.09.014
    [5] KE Y, SUKTHANKAR R. PCA-SIFT: a more distinctive representation for local image descriptors[C]//Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). Washington, DC, USA: IEEE, 2004: 506–513.
    [6] RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB: an efficient alternative to SIFT or SURF[C]//Proceedings of 2011 International Conference on Computer Vision (ICCV). Barcelona, Spain: IEEE, 2011: 2564–2571.
    [7] NEGAHDARIPOUR S, SARAFRAZ A. Improved stereo matching in scattering media by incorporating a backscatter cue[J]. IEEE Transactions on Image Processing, 2014, 23(12): 5743–5755. doi: 10.1109/TIP.2014.2358882
    [8] 李雅倩, 张岩松, 李 海滨, 等. 基于深度约束的水下稠密立体匹配[J]. 光子学报, 2017, 46(7): 0715001-1−0715001-10. doi: 10.3788/gzxb20174607.0715001

    LI Y Q, ZHANG Y S, LI H B, et al. Underwater dense stereo matching based on depth constraint[J]. Acta Photonica Sinica, 2017, 46(7): 0715001-1−0715001-10 (in Chinese). doi: 10.3788/gzxb20174607.0715001
    [9] GEDGE J. Underwater stereo matching and its calibration[D]. Edmonton: University of Alberta, 2011.
    [10] FERREIRA R, COSTEIRA J P, SANTOS J A. Stereo reconstruction of a submerged scene[M]//MARQUES J S, PÉREZ DE LA BLANCA N, PINA P. Pattern Recognition and Image Analysis. Berlin, Heidelberg: Springer, 2005: 102–109.
    [11] 张强, 郝凯, 李海滨. 水下环境中基于曲线约束的SIFT特征匹配算法研究[J]. 光学学报, 2014, 34(2): 0215003-1−0215003-7. doi: 10.3788/AOS201434.0215003

    ZHANG Q, HAO K, LI H B. Research on scale invariant feature transform feature matching based on underwater curve constraint[J]. Acta Optica Sinica, 2014, 34(2): 0215003-1−0215003-7 (in Chinese). doi: 10.3788/AOS201434.0215003
    [12] 李佳宽, 孙春生, 胡艺铭, 等. 一种基于ORB特征的水下立体匹配方法[J]. 光电工程, 2019, 46(4): 180456.

    LI J K, SUN C S, HU Y M, et al. An underwater stereo matching method based on ORB features[J]. Opto-Electronic Engineering, 2019, 46(4): 180456 (in Chinese).
    [13] CHEN H H, WANG C C, SHIU D C, et al. A preliminary study on positioning of an underwater vehicle based on feature matching of seafloor images[C]//2018 OCEANS-MTS/IEEE Kobe Techno-Oceans (OTO). Kobe, Japan: IEEE, 2018: 1–6.
    [14] ROSTEN E, PORTER R, DRUMMOND T. Faster and better: a machine learning approach to corner detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(1): 105–119. doi: 10.1109/TPAMI.2008.275
    [15] 张岩松. 基于深度约束的水下立体匹配研究[D]. 秦皇岛: 燕山大学, 2017.

    ZHANG Y S. Researching on underwater stereo matching based on depth constraint[D]. Qinhuangdao: Yanshan University, 2017 (in Chinese).
    [16] 刘士荣, 吴楚, 张波涛, 等. 基于极线约束SIFT特征和粒子滤波的目标跟踪算法[J]. 上海交通大学学报, 2014, 48(7): 1026–1032,1038.

    LIU S R, WU C, ZHANG B T, et al. A tracking algorithm based on SIFT feature and particle filter with epipolar constraint[J]. Journal of Shanghai Jiaotong University, 2014, 48(7): 1026–1032,1038 (in Chinese).
    [17] 李炼, 李维嘉, 吴耀中. 基于红色暗通道先验理论与CLAHE算法的水下图像增强算法[J]. 中国舰船研究, 2019, 14(增刊 1): 175–182. doi: 10.19693/j.issn.1673-3185.01508

    LI L, LI W J, WU Y Z. An underwater image enhancement algorithm based on RDCP and CLAHE[J]. Chinese Journal of Ship Research, 2019, 14(Supp 1): 175–182 (in Chinese). doi: 10.19693/j.issn.1673-3185.01508
    [18] SKINNER K A, ZHANG J M, OLSON E A, et al. UWStereoNet: unsupervised learning for depth estimation and color correction of underwater stereo imagery[C]//Proceedings of 2019 International Conference on Robotics and Automation (ICRA). Montreal, QC, Canada: IEEE, 2019: 7947–7954.
    [19] SWIRSKI Y, SCHECHNER Y Y. 3Deflicker from motion[C]//Proceedings of IEEE International Conference on Computational Photography (ICCP). Cambridge, MA, USA: IEEE, 2013: 1–9.
  • 加载中
图(7) / 表(2)
计量
  • 文章访问数:  176
  • HTML全文浏览量:  89
  • PDF下载量:  23
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-11-23
  • 修回日期:  2021-03-26
  • 网络出版日期:  2021-11-09
  • 刊出日期:  2021-12-20

目录

    /

    返回文章
    返回