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改进窗口特征及微分算子的立体匹配算法

Stereo Matching by Improved Window Characteristics and Differential Operators

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摘要

针对ELAS(Efficient Large-Scale Stereo Matching)算法视差图条纹明显且具有空洞区域的问题,提出一种匹配窗口特性与微分特性相结合的局部立体匹配算法,增强描述子对点特征信息的描述能力,为待匹配点提供更有区分度的相似性度量。先根据彩色图像的经典自适应算法,从空间上提出适应于灰度图像的窗口描述子,依据图像信号的特点,从像素层面上选择平滑性更小的微分算子。再将匹配窗口与微分算子相结合,获得比只使用两者之一更强的特性信息描述能力。最后进行标准数据集的客观检验与自采集图像的主观评价,结果表明该算法具有较强的鲁棒性和更高的匹配精度,明显改善了原匹配策略视差图中出现条纹及空洞的现象。

Abstract

The parallax pattern obtained from the ELAS (efficient large-scale stereo matching) algorithm contains obvious fringes and void regions. To address this problem, a stereo matching algorithm that combines matching window characteristics with differentials is proposed in this paper. By enhancing the description of the feature information of points, the similarity measure of the points to be matched is provided with a higher degree of discrimination. First, according to the classical adaptive algorithm of color images, a window descriptor adapted to a gray image was proposed spatially. Next, according to the characteristics of an image signal, a less smooth differential operator was selected at the pixel level. Then the proposed matching window was combined with a differential operator to obtain a description ability of feature information stronger than either of the two. Finally, objective evaluation of standard benchmarks and subjective evaluation of self-collected images show that the proposed algorithm is more robust and has higher matching accuracy, and it obviously improves phenomena related to stripes and void regions in the disparity map.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TN911.73

DOI:10.3788/LOP57.081504

所属栏目:机器视觉

基金项目:国家自然科学基金、国家自然科学基金委员会“共融机器人基础理论与关键技术研究”重大研究计划、辽宁省教育厅科学研究一般项目、辽宁省自然科学基金面上项目;

收稿日期:2019-08-29

修改稿日期:2019-09-24

网络出版日期:2020-04-01

作者单位    点击查看

李新春:辽宁工程技术大学电子与信息工程学院, 辽宁 葫芦岛 125105
殷新勇:辽宁工程技术大学电子与信息工程学院, 辽宁 葫芦岛 125105
林森:辽宁工程技术大学电子与信息工程学院, 辽宁 葫芦岛 125105中国科学院沈阳自动化研究所机器人学国家重点实验室, 辽宁 沈阳 110016中国科学院机器人与智能制造创新研究院, 辽宁 沈阳 110016

联系人作者:殷新勇(xin-yong.yin@outlook.com)

备注:国家自然科学基金、国家自然科学基金委员会“共融机器人基础理论与关键技术研究”重大研究计划、辽宁省教育厅科学研究一般项目、辽宁省自然科学基金面上项目;

【1】Mayer N, Ilg E, Hausser P, et al. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation . [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, LasVegas, NV, USA. New York: IEEE. 2016, 4040-4048.

【2】Kendall A, Martirosyan H, Dasgupta S, et al. End-to-end learning of geometry and context for deep stereo regression . [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE. 2017, 66-75.

【3】Chang J R, Chen Y S. Pyramid stereo matching network . [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE. 2018, 5410-5418.

【4】Li D H, Shen H Y, Yu X, et al. Binocular ranging method using stereo matching based on improved Census transform [J]. Laser & Optoelectronics Progress. 2019, 56(11): 111503.
李大华, 沈洪宇, 于晓, 等. 一种改进Census变换的双目匹配测距方法 [J]. 激光与光电子学进展. 2019, 56(11): 111503.

【5】Wang Q, Piao Y. Depth image acquisition technology based on improved genetic algorithm [J]. Laser & Optoelectronics Progress. 2018, 55(2): 021003.
王琦, 朴燕. 基于改进遗传算法的深度图像获取技术 [J]. 激光与光电子学进展. 2018, 55(2): 021003.

【6】Zhang Y, Khamis S, Rhemann C, et al. Active Stereo Net: end-to-end self-supervised learning for active stereo systems . [C]∥Proceedings of the European Conference on Computer Vision (ECCV), September 8-14, 2018, Munich, Germany. New York: IEEE. 2018, 784-801.

【7】?bontar J. LeCun Y. Computing the stereo matching cost with a convolutional neural network . [C]∥Proceedings of the IEEE conference on computer vision and pattern recognition, June 7-12, 2015, Boston, USA. New York: IEEE. 2015, 1592-1599.

【8】Xiao J S, Tian H, Zou W T, et al. Stereo matching based on convolutional neural network [J]. Acta Optica Sinica. 2018, 38(8): 0815017.
肖进胜, 田红, 邹文涛, 等. 基于深度卷积神经网络的双目立体视觉匹配算法 [J]. 光学学报. 2018, 38(8): 0815017.

【9】Spyropoulos A, Komodakis N, Mordohai P. Learning to detect ground control points for improving the accuracy of stereo matching . [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, Ohio. New York: IEEE. 2014, 1621-1628.

【10】Taniai T, Matsushita Y, Naemura T. Graph cut based continuous stereo matching using locally shared labels . [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, Ohio. New York: IEEE. 2014, 1613-1620.

【11】Sinha S N, Scharstein D, Szeliski R. Efficient high-resolution stereo matching using local plane sweeps . [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, Ohio. New York: IEEE. 2014, 1582-1589.

【12】Zhang K, Fang Y, Min D, et al. Cross-scale cost aggregation for stereo matching . [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, Ohio. New York: IEEE. 2014, 1590-1597.

【13】Pang J, Sun W, Yang C, et al. Zoom and learn: generalizing deep stereo matching to novel domains . [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 18-22, 2018, Salt Lake City, Utah. New York: IEEE. 2018, 2070-2079.

【14】Tonioni A, Tosi F, Poggi M, et al. Real-time self-adaptive deep stereo . [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 16-20, 2019, Long Beach, CA. New York: IEEE. 2019, 195-204.

【15】Geiger A, Roser M, Urtasun R. Efficient large-scale stereo matching [M]. ∥Kimmel R, Klette R, Sugimoto A. Computer vision-ACCV 2010. Lecture notes in computer science. Heidelberg: Springer. 2010, 6492: 25-38.

【16】Wu P L, Li Y N, Yang F, et al. A CLM-based method of indoor affordance areas classification for service robots [J]. Robot. 2018, 40(2): 188-194.
吴培良, 李亚南, 杨芳, 等. 一种基于CLM的服务机器人室内功能区分类方法 [J]. 机器人. 2018, 40(2): 188-194.

【17】Wu S, Xu J Z, Zhang Y X, et al. Reliability evaluation method and application for light-stripe-center extraction [J]. Acta Optica Sinica. 2011, 31(11): 1115001.
王顺, 徐静珠, 张益昕, 等. 结构光光条中心点信度评价方法与应用 [J]. 光学学报. 2011, 31(11): 1115001.

【18】Scharstein D, Szeliski R. -10-15)[2019-08-26] . http:∥vision.middlebury.edu/stereo/. 2014.

【19】Guo X, Yang K, Yang W, et al. Group-wise correlation stereo network . [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 16-20, 2019, Long Beach, CA. New York: IEEE. 2019, 3273-3282.

【20】Zhang F, Prisacariu V, Yang R, et al. GA-Net: guided aggregation net for end-to-end stereo matching . [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 16-20, 2019, Long Beach, CA. New York: IEEE. 2019, 185-194.

引用该论文

Li Xinchun,Yin Xinyong,Lin Sen. Stereo Matching by Improved Window Characteristics and Differential Operators[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081504

李新春,殷新勇,林森. 改进窗口特征及微分算子的立体匹配算法[J]. 激光与光电子学进展, 2020, 57(8): 081504

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