Fast optical flow estimation

被引:0
|
作者
Charif, F.
Baarir, Z. -E.
机构
关键词
D O I
暂无
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
This paper presents a fast, accurate and reliable approach for robust boundary preserving estimation of optical flow. Several studies have addressed this topic and proposed methods that account for velocity boundaries at the cost of significant computational complexity, which makes them inadequate for current real-time applications. The proposed method is derived from the benchmark algorithm of Horn & Schnuck and Simoncelli's matched-pair 5 tap filters, such that it produces robust, fast and exact detection of motion boundaries and it is very simple to implement. Experimental results using synthetic and real optical flows are presented to demonstrate the effectiveness of our method in comparison to selected methods.
引用
收藏
页码:147 / 153
页数:7
相关论文
共 50 条
  • [41] Dynamically consistent optical flow estimation
    Papadakis, Nicolas
    Corpetti, Thomas
    Memin, Etienne
    2007 IEEE 11TH INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS 1-6, 2007, : 481 - 487
  • [42] Fast two-frame multiscale dense optical flow estimation using discrete wavelet filters
    Liu, HY
    Chellappa, R
    Rosenfeld, A
    JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 2003, 20 (08) : 1505 - 1515
  • [43] Deep Equilibrium Optical Flow Estimation
    Bai, Shaojie
    Geng, Zhengyang
    Savani, Yash
    Kolter, J. Zico
    2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022), 2022, : 610 - 620
  • [44] Adaptive multiscale optical flow estimation
    Li, Jian
    Benton, Christopher P.
    Nikolov, Stavri G.
    Scott-Samuel, Nicholas E.
    2007 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-7, 2007, : 1073 - +
  • [45] Consistent segmentation for optical flow estimation
    Zitnick, CL
    Jojic, N
    Kang, SB
    TENTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS 1 AND 2, PROCEEDINGS, 2005, : 1308 - 1315
  • [46] Optical Flow Estimation with Channel Constancy
    Sevilla-Lara, Laura
    Sun, Deqing
    Learned-Miller, Erik G.
    Black, Michael J.
    COMPUTER VISION - ECCV 2014, PT I, 2014, 8689 : 423 - 438
  • [47] Measuring confidence in optical flow estimation
    BainbridgeSmith, A
    Lane, RG
    ELECTRONICS LETTERS, 1996, 32 (10) : 882 - 884
  • [48] Learning optical flow for fast MRI reconstruction
    Schmoderer, Timothee
    Aviles-Rivero, Angelica, I
    Corona, Veronica
    Debroux, Noemie
    Schonlieb, Carola-Bibiane
    INVERSE PROBLEMS, 2021, 37 (09)
  • [49] Fast incorporation of optical flow into active polygons
    Unal, G
    Krim, H
    Yezzi, A
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2005, 14 (06) : 745 - 759
  • [50] Fast Image Mosaicking using Optical Flow
    Sapra, Karan
    Birchfield, Stanley T.
    IEEE SOUTHEASTCON 2011: BUILDING GLOBAL ENGINEERS, 2011, : 260 - 263