Image Stitching Based on DPP Improved RANSAC Algorithm
CSTR:
Author:
  • Article
  • | |
  • Metrics
  • |
  • Reference [19]
  • |
  • Related [20]
  • | | |
  • Comments
    Abstract:

    To improve the speed and precision of registration in image stitching, this study proposes a modified RANSAC algorithm based on Determinantal Point Processes (DPP), aiming to tackle the issue of robustness model estimation. This method utilizes global negative correlation of the DPP sampling to model matching feature points, eliminates those incorrect matching points, and therefore realizes the homogenization and decentralization of the sampling. The point set extracted in DPP is used as the input of RANSAC to elicit transformation matrix. Experimental results show that compared with traditional RANSAC algorithm, this algorithm ensures higher accuracy and robustness, which greatly enhances the efficiency of automatic image stitching.

    Reference
    [1] 张亚娟. 基于SURF特征的图像与视频拼接技术的研究[硕士学位论文]. 西安:西安电子科技大学, 2013.
    [2] Armangué X, Salvi J. Overall view regarding fundamental matrix estimation. Image and Vision Computing, 2003, 21(2):205-220.[DOI:10.1016/S0262-8856(02)00154-3]
    [3] 魏若岩, 阮晓钢. 于乃功, 等. 基于Skinner操作条件反射的抽样一致性算法. 控制与决策, 2015, 30(2):235-240.
    [4] 唐永鹤, 胡旭峰, 卢焕章. 应用序贯相似检测的基本矩阵快速鲁棒估计. 光学精密工程, 2011, 19(11):2759-2766.
    [5] Torr PHS, Zisserman A. MLESAC:A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding, 2000, 78(1):138-156.[DOI:10.1006/cviu.1999.0832]
    [6] Chum O, Matas J. Matching with PROSAC-progressive sample consensus. Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA. 2005. 220-226.
    [7] Hast A, Nysjö J, Marchetti A. Optimal ransac-towards a repeatable algorithm for finding the optimal set. Journal of WSCG, 2015, 21(1):21-30
    [8] Lowe DG. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2):91-110.[DOI:10.1023/B:VISI.0000029664.99615.94]
    [9] 徐敏, 莫东鸣, 张祯. 高斯二阶差分特征算子在图像拼接中的应用. 计算机系统应用, 2016, 25(4):167-173.
    [10] Brown M, Szeliski R, Winder S. Multi-image matching using multi-scale oriented patches. Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA. 2005. 510-517.
    [11] Fischler MA, Bolles RC. Random sample consensus:A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 1981, 24(6):381-395.[DOI:10.1145/358669.358692]
    [12] Kulesza A, Taskar B. Determinantal point processes for machine learning. Foundations and Trends in Machine Learning, 2012, 5(2-3):123-286.[DOI:10.1561/2200000044]
    [13] Johansson K. Determinantal processes with number variance saturation. Communications in Mathematical Physics, 2004, 252(1-3):111-148.[DOI:10.1007/s00220-004-1186-4]
    [14] Kulesza A, Taskar B. k-DPPs:Fixed-size determinantal point processes. Proceedings of the 28th International Conference on Machine Learning. Bellevue, WA, USA. 2011. 1193-1200.
    [15] Decreusefond L, Flint I, Privault N, et al. Determinantal point processes. Peccati G, Reitzner M. Stochastic Analysis for Poisson Point Processes. Cham:Springer, 2016. 311-342.
    [16] Shao H, Chen S, Zhao JY, et al. Face recognition based on subset selection via metric learning on manifold. Frontiers of Information Technology & Electronic Engineering, 2015, 16(12):1046-1058.
    [17] Yu TS, Wang RS. Graph matching with low-rank regularization. Proceedings of 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). Lake Placid, NY, USA. 2016. 1-9.
    [18] Tong RF, Zhang Y, Cheng KL. StereoPasting:Interactive composition in stereoscopic images. IEEE Trans. on Visualization and Computer Graphics, 2013, 19(8):1375-1385.[DOI:10.1109/TVCG.2012.319]
    [19] Adwan S, Alsaleh I, Majed R. A new approach for image stitching technique using Dynamic Time Warping (DTW) algorithm towards scoliosis X-ray diagnosis. Measurement, 2016, (84):32-46.
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

汪旌,张赟,陈爽.基于DPP改进RANSAC算法的图像拼接.计算机系统应用,2018,27(5):112-118

Copy
Share
Article Metrics
  • Abstract:2102
  • PDF: 2433
  • HTML: 1345
  • Cited by: 0
History
  • Received:September 15,2017
  • Revised:September 30,2017
  • Online: April 23,2018
Article QR Code
You are the first990431Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address:4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code:100190
Phone:010-62661041 Fax: Email:csa (a) iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063