Robust Optic Flow Computation

被引:0
|
作者
Alireza Bab-Hadiashar
David Suter
机构
[1] Monash University,Intelligent Robotics Research Centre, Department of Electrical and Computer Systems Engineering
关键词
Image Processing; Artificial Intelligence; Computer Vision; Linear Equation; Computer Image;
D O I
暂无
中图分类号
学科分类号
摘要
This paper formulates the optic flow problem as a set of over-determined simultaneous linear equations. It then introduces and studies two new robust optic flow methods. The first technique is based on using the Least Median of Squares (LMedS) to detect the outliers. Then, the inlier group is solved using the least square technique. The second method employs a new robust statistical method named the Least Median of Squares Orthogonal Distances (LMSOD) to identify the outliers and then uses total least squares to solve the optic flow problem. The performance of both methods are studied by experiments on synthetic and real image sequences. These methods outperform other published methods both in accuracy and robustness.
引用
收藏
页码:59 / 77
页数:18
相关论文
共 50 条
  • [31] Robust optical flow computation based on least-median-of-squares regression
    Ong, EP
    Spann, M
    INTERNATIONAL JOURNAL OF COMPUTER VISION, 1999, 31 (01) : 51 - 82
  • [32] Robust Computation of Determinant
    Ogita, Takeshi
    INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2009 (ICCMSE 2009), 2012, 1504 : 1119 - 1123
  • [33] Robust consensus computation
    Rausch, Tobias
    Emde, Anne-Katrin
    Reinert, Knut
    BMC BIOINFORMATICS, 2008, 9 (Suppl 10)
  • [34] Robust Optical Flow Computation Based on Least-Median-of-Squares Regression
    E.P. Ong
    M. Spann
    International Journal of Computer Vision, 1999, 31 : 51 - 82
  • [35] Robust computation of optical flow under non-uniform illumination variations
    Teng, CH
    Lai, SH
    Chen, YS
    Hsu, WH
    16TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL I, PROCEEDINGS, 2002, : 327 - 330
  • [36] Biologically motivated space-variant filtering for robust optic flow processing
    Calow, D.
    Kruger, N.
    Worgotter, F.
    Lappe, M.
    NETWORK-COMPUTATION IN NEURAL SYSTEMS, 2005, 16 (04) : 323 - 340
  • [37] Robust Models for Optic Flow Coding in Natural Scenes Inspired by Insect Biology
    Brinkworth, Russell S. A.
    O'Carroll, David C.
    PLOS COMPUTATIONAL BIOLOGY, 2009, 5 (11)
  • [38] HUMANS COMBINE THE OPTIC FLOW WITH STATIC DEPTH CUES FOR ROBUST PERCEPTION OF HEADING
    VANDENBERG, AV
    BRENNER, E
    VISION RESEARCH, 1994, 34 (16) : 2153 - 2167
  • [39] Differential Tuning to Visual Motion Allows Robust Encoding of Optic Flow in the Dragonfly
    Evans, Bernard J. E.
    O'Carroll, David C.
    Fabian, Joseph M.
    Wiederman, Steven D.
    JOURNAL OF NEUROSCIENCE, 2019, 39 (41): : 8051 - 8063
  • [40] Competitive Dynamics in MSTd: A Mechanism for Robust Heading Perception Based on Optic Flow
    Layton, Oliver W.
    Fajen, Brett R.
    PLOS COMPUTATIONAL BIOLOGY, 2016, 12 (06)