基于灰度塔评分的匹配模型构建在无人机网络视频拼接中的应用

doi:10.11772/j.issn.1001-9081.2018092034

计算机应用 ›› 2019, Vol. 39 ›› Issue (5): 1480-1484.DOI: 10.11772/j.issn.1001-9081.2018092034

• 虚拟现实与多媒体计算 • 上一篇下一篇

基于灰度塔评分的匹配模型构建在无人机网络视频拼接中的应用

李南云¹, 王旭光², 吴华强³, 何青林³

1. 中国科学技术大学苏州纳米技术与纳米仿生学院, 合肥 230026;
2. 中国科学院苏州纳米技术与纳米仿生研究所, 江苏苏州 215123;
3. 清华大学微电子学研究所, 北京 100084

收稿日期:2018-10-09 修回日期:2018-12-17 出版日期:2019-05-10 发布日期:2019-05-14
通讯作者: 王旭光
作者简介:李南云(1997-),女,安徽宿州人,硕士研究生,主要研究方向:图像处理、视频拼接;王旭光(1976-),男,吉林长春人,研究员,博士,主要研究方向:人脸识别、半导体存储;吴华强(1978-),男,安徽黄山人,教授,博士,主要研究方向:新型半导体存储器、基于新型器件的类脑计算;何青林(1993-),男,重庆人,硕士研究生,主要研究方向:视频处理、视频拼接。

Application of matching model based on grayscale tower score in unmanned aerial vehicle network video stitching

LI Nanyun¹, WANG Xuguang², WU Huaqiang³, HE Qinglin³

1. Suzhou Nanotechnology and Nano-Bionics College, University of Science and Technology of China, Hefei Anhui 230026, China;
2. Suzhou Institute of Nano-Tech and Nano-Bionics(SINANO), Chinese Academy of Sciences, Suzhou Jiangsu 215123, China;
3. Institute of Microelectronics, Tsinghua University, Beijing 100084, China

Received:2018-10-09 Revised:2018-12-17 Online:2019-05-10 Published:2019-05-14

摘要/Abstract

摘要： 对于复杂非配合情况下，视频拼接中特征匹配对的数目和特征匹配准确率无法同时达到后续稳像和拼接的要求这一问题，提出一种基于灰度塔对特征点进行评分后构建匹配模型来进行精准特征匹配的方法。首先，利用灰度级压缩后相近灰度级合并这一现象，建立灰度塔来实现对特征点的评分；而后，选取评分高的特征点建立基于位置信息的匹配模型；最后，依据匹配模型的定位进行区域分块匹配来避免全局特征点的干扰和大误差噪点匹配，选择误差最小的特征匹配对作为最终结果匹配对。另外，在运动的视频流中，可通过前后帧信息建立掩模进行区域特征提取，匹配模型也可选择性遗传给后帧以节约算法时间。实验结果表明，在运用了基于灰度塔评分的匹配模型后，特征匹配对准确率在95%左右。相同帧特征匹配对的数目相较于随机采样一致性有近10倍的提升，在兼顾匹配数目和匹配准确率的同时且无大误差匹配结果，对于环境和光照有较好的鲁棒性。

关键词: 特征提取, 特征匹配, 视频拼接, 灰度塔, 匹配模型, 分块匹配

Abstract: Concerning the problem that in complex and non-cooperative situations the number of matching feature pairs and the accuracy of feature matching results in video stitching can not meet the requirements of subsequent image stabilization and stitching at the same time, a method of constructing matching model to match features accurately after feature points being scored by grayscale tower was proposed. Firstly, the phenomenon that the similiar grayscales would merged together after grayscale compression was used to establish a grayscale tower to realize the scoring of feature points. Then, the feature points with high score were selected to establish the matching model based on position information. Finally, according to the positioning of the matching model, regional block matching was performed to avoid the influence of global feature point interference and large error noise matching, and the feature matching pair with the smallest error was selected as the final result of matching pair. In addition, in a motion video stream, regional feature extraction could be performed by using the information of previous and next frames to establish a mask, and the matching model could be selectively passed on to the next frame to save the computation time. The simulation results show that after using this matching model based on grayscale tower score, the feature matching accuracy is about 95% and the number of matching feature pairs of the same frame is nearly 10 times higher than that of the traditional method. The proposed method has good robustness to environment and illumination while guaranteeing the matching number and the matching accuracy without large error matching result.

Key words: feature extraction, feature matching, video stitching, grayscale tower, matching model, block matching

中图分类号:

TP391.4

李南云, 王旭光, 吴华强, 何青林. 基于灰度塔评分的匹配模型构建在无人机网络视频拼接中的应用[J]. 计算机应用, 2019, 39(5): 1480-1484.

LI Nanyun, WANG Xuguang, WU Huaqiang, HE Qinglin. Application of matching model based on grayscale tower score in unmanned aerial vehicle network video stitching[J]. Journal of Computer Applications, 2019, 39(5): 1480-1484.

参考文献

[1] IVAN S K, OLEG V P. Spherical video panorama stitching from multiple cameras with intersecting fields of view and inertial measurement unit[C]// Proceedings of the 2016 International Siberian Conference on Control and Communications. Piscataway, NJ: IEEE, 2016: 1-6.
[2] ZHANG L, HE Z, LIU Y. Deep object recognition across domains based on adaptive extreme learning machine[J]. Neurocomputing, 2017,239:194-203.
[3] YANG B, DONG Z, LIANG F, et al. Automatic registration of large-scale urban scene point clouds based on semantic feature points[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2016, 113: 43-58.
[4] LOWE D G. Distinctive image features from scale-invariant key-points[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
[5] HSU W Y, LEE Y C. Rat brain registration using improved speeded up robust features[J]. Journal of Medical and Biological Engineering, 2017, 37(1): 45-42.
[6] RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB: an efficient alternative to SIFT or SURF[C]// Proceedings of the 2011 International Conference on Computer Vision. Washington, DC: IEEE Computer Society, 2011:2564-2571.
[7] LEUTENEGGER S, CHLI M, SIEGWART Y. BRISK: binary robust invariant scalable keypoints[C]// Proceedings of the 2011 International Conference on Computer Vision. Washington, DC: IEEE Computer Society, 2011:2548-2555.
[8] CALONDER M, LEPETIT V, STRECHA C, et al. BRIEF: binary robust independent elementary features[C]// Proceedings of the 11th European Conference on Computer Vision. Berlin: Springer, 2010: 778-792.
[9] MOR M, FRAENKEL A S. A hash code method for detecting and correcting spelling-errors[J]. Communications of the ACM, 1982, 25(12): 935-938.
[10] SANTHA T, MOHANA M B V. The significance of real-time,biomedical and satellite image processing in understanding the objects & application to computer vision[C]// Proceedings of the 2nd IEEE International Conference on Engineering & Technology. Piscataway, NJ: IEEE, 2016: 661-670.
[11] BROWN M, LOWE D G. Automatic panoramic image stitching using invariant features[J]. International Journal of Computer Vision, 2007, 74(1):59-73.
[12] GUO H, LIU S, HE T, et al. Joint video stitching and stabilization from moving cameras[J]. IEEE Transactions on Image Processing, 2016, 25(11):5491-5503.
[13] 倪国强, 刘琼. 多源图像配准技术分析与展望[J].光电工程, 2004, 31(9):1-6.(NI G Q,LIU Q. Analysis and prospect of multi-source image registration techniques[J].Opto-Electronic Engineering, 2004,31(9):1-6.)
[14] ZARAGOZA J, CHIN T J, BROWN M S, et al. As-projective-as-possible image stitching with moving DLT[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(7):1285-1298.
[15] 朱云芳, 叶秀清, 顾伟康.视频序列的全景图拼接技术[J].中国图象图形学报,2006,11(8):1150-1155.(ZHU Y F,YE X Q,GU W K. Mosaic panorama technique for videos[J]. Journal of Image and Graphics,2006,11(8):1150-1155.)

基于灰度塔评分的匹配模型构建在无人机网络视频拼接中的应用

Application of matching model based on grayscale tower score in unmanned aerial vehicle network video stitching

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