A sub-region image registration algorithm with weight-based hierarchical importance sample consensus for moving target detection

References

• Kim J, Ye G, Kim D. Moving object detection under free-moving camera. 2010 IEEE International Conference on Image Processing; 2010. p. 4669–4672.
   Google Scholar
• Cheraghi SA, Sheikh UU. Moving object detection using image registration for a moving camera platform. 2012 IEEE International Conference on Control System, Computing and Engineering; Nov. 2012. p. 355–359.
   Google Scholar
• Lin CC, Wolf M. Detecting moving objects using a camera on a moving platform. 20th International Conference on Pattern Recognition; Aug. 2010. p. 460–463.
   Google Scholar
• Walha A, wali A, Alimi AM. Video stabilization with moving object detecting and tracking for aerial video surveillance. Int J Multimedia Tools Appl. 2014;74(17):6745–6767. doi: 10.1007/s11042-014-1928-z
   Web of Science ®Google Scholar
• Liang R, Yan L, Gao P. Aviation video moving-target detection with inter-frame difference. 2010 3rd International Congress on Image and Signal Processing (CISP), Vol. 3, Oct. 2010. p. 1494–1497.
   Google Scholar
• Chen Y, Wu Q. Moving vehicle detection based on optical flow estimation of edge. 2015 11th International Conference on Natural Computation (ICNC); Aug. 2015. p. 754–758.
   Google Scholar
• Barnich O, Van Droogenbroeck M. ViBe: a universal background subtraction algorithm for video sequences. IEEE T Image Process. 2011;20(6):1709–1724. doi: 10.1109/TIP.2010.2101613
   PubMed Web of Science ®Google Scholar
• Flusser J, Zitova B. Image registration methods: a survey. Image Vision Comput. 2003;21(11):977–1000. doi: 10.1016/S0262-8856(03)00137-9
   Web of Science ®Google Scholar
• Pluim JPW, Maintz JBA, Viergever MA. Mutual information based registration of medical images: a survey. IEEE T Med Imaging 2003;22(8):986–1004.
   Google Scholar
• Chen S, Guo Q, Leung H. A maximum likelihood approach to joint image registration and fusion. IEEE T Image Process. 2011;20(5):1363–1372. doi: 10.1109/TIP.2010.2090530
   PubMed Web of Science ®Google Scholar
• Liu Z, An J, Jing Y. A simple and robust feature point matching algorithm based on restricted spatial order constraints for aerial image registration. IEEE T Geosci Remote. 2012;50(2):514–527. doi: 10.1109/TGRS.2011.2160645
   Web of Science ®Google Scholar
• He Y, Yap KH, Chen LA. Nonlinear least square technique for simultaneous image registration and super-resolution. IEEE T Image Process. 2007;16:2830–2841. doi: 10.1109/TIP.2007.908074
   PubMed Web of Science ®Google Scholar
• Wu G, Kim M, Wang, Q. Scalable high-performance image registration framework by unsupervised deep feature representations learning. IEEE T Bio-Med Eng. 2015;63(7):1–1.
   Web of Science ®Google Scholar
• Fishier MA, Boles RC. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM. 1981;24(6):381–395. doi: 10.1145/358669.358692
   Web of Science ®Google Scholar
• Araki S, Matsuoka T, Takemura H. Real-time tracking of multiple moving objects in moving camera image sequences using robust statistics. Fourteenth Int Conf Pattern Recogn. 1998;2:1433–1435. doi: 10.1109/ICPR.1998.711972
   Google Scholar
• Chum O, Matas J. Matching with PROSAC-Progressive Sample Consensus. IEEE Comput Soc Conf Comput Vis Pattern Recogn. 2005;1:220–226.
   Google Scholar
• Zhang L, Rastgar H, Wang D. Maximum likelihood estimation sample consensus with validation of individual correspondences. 5th International Symposium ISVC 2009; Dec. 2009. p. 447–456.
   Google Scholar
• Benedetti A, Perona P. Real-time 2-D feature detection on a reconfigurable computer. IEEE Computer Society Conference on Computer Vision and Pattern Recognition IEEE Computer Society; 1998. p. 586.
   Google Scholar
• Zheng Y, Doermann D. Robust point matching for nonrigid shapes by preserving local neighborhood structures. IEEE T Pattern Anal. 2006;28(4):643–649. doi: 10.1109/TPAMI.2006.81
   PubMed Web of Science ®Google Scholar
• Kim T, Im Y. Automatic satellite image registration by combination of matching and random sample consensus. IEEE T Geosci Remote. 2003;41(5):1111–1117. doi: 10.1109/TGRS.2003.811994
   Web of Science ®Google Scholar
• Chum O, Matas J. Optimal randomized RANSAC. IEEE T Pattern Anal. 2008;30(8):1472–1482. doi: 10.1109/TPAMI.2007.70787
   PubMed Web of Science ®Google Scholar
• Song Z, Zhou S, Guan JA. Novel image registration algorithm for remote sensing under affine transformation. IEEE T Geosci Remote. 2014;52:4895–4912. doi: 10.1109/TGRS.2013.2285814
   Web of Science ®Google Scholar
• Vaidya OS, Kumar S. Analytic hierarchy process: an overview of applications. Eur J Oper Res. 2006;169(1):1–29. doi: 10.1016/j.ejor.2004.04.028
   Web of Science ®Google Scholar
• Dodge Y. Bernoulli’s theorem, in the concise encyclopedia of statistics. New York (NY): Springer; 2008, pp. 39.
   Google Scholar
• Zhang H, Li F, Li Na. Compressed-domain-based no-reference video quality assessment model considering fast motion and scene change. Multimedia Tools and Application, published online. May 2016. doi:10.1007/s11042-016-3558-0.
   Google Scholar
• Xu M, Lu J. Distributed RANSAC for the robust estimation of three-dimensional reconstruction. IET Comput Vis. 2012;6(4):324–333. doi: 10.1049/iet-cvi.2010.0223
   Web of Science ®Google Scholar
• Yang J, Schonfeld D, Mohamed M. Robust video stabilization based on particle filter tracking of projected camera motion. IEEE T Circ Syst Vid. 2009;19(7):945–954. doi: 10.1109/TCSVT.2009.2020252
   Web of Science ®Google Scholar