Optical-flow based on an edge-avoidance procedure |
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| Affiliation: | 1. Département d’Informatique, Université de Sherbrooke, 2500 Boul. de l’Université, Sherbrooke, Que., Canada J1K 2R1;2. Département d’Informatique et de Recherche Opérationnelle (DIRO), Université de Montréal, P.O. Box 6128, Stn. Centre-Ville, Montréal, Que., Canada H3C 3J7;1. University of Ulster, Magee, Londonderry BT48 7JL, Northern Ireland, UK;2. University of Ulster, Coleraine BT52 1SA, Northern Ireland, UK;1. Max Planck Institute of Neurobiology, Am Klopferspitz 18, 82152 Martinsried, Germany;2. Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA 20147, USA;1. College of Physical Science and Technology, Key Laboratory of Radiation Physics and Technology, Ministry of Education, Sichuan University, Chengdu, PR China;2. Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Tx, USA;3. Department of Electrical and Computer Engineering, Rice University, Houston, Tx, USA;4. Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Tx, USA;5. Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Tx, USA;2. State Key Laboratory of Precision Measuring Technology and Instrument, Tianjin University, Tianjin 300072, People׳s Republic of China;2. Beijing Center for Mathematics and Information Interdisciplinary Sciences (BCMIIS), Beijing 100089, People׳s Republic of China;1. Department of Electronics and Communication Engineering, Zhejiang Normal University, Jinhua 321004, PR China;2. Chair of Systems Design, ETH Zürich, Weinbergstrasse 56/58, CH-8092 Zürich, Switzerland;3. Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris 75015, France;4. Department of Computer Sciences, Zhejiang Normal University, Jinhua 321004, PR China |
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| Abstract: | This paper presents a differential optical flow method which accounts for two typical motion-estimation problems: (1) flow regularization within regions of uniform motion while (2) preserving sharp edges near motion discontinuities i.e., where motion is multimodal by nature. The method proposed is a modified version of the well known Lucas–Kanade (LK) algorithm. While many edge-preserving strategies try to minimize the effect of outliers by using a line process or a robust function, our method takes a novel approach to solve the problem. Based on documented assumptions, our method computes motion with a classical least-squares fit on a local neighborhood shifted away from where motion is likely to be multimodal. In this way, the inherent bias due to multiple motion around moving edges is avoided instead of being compensated. This edge-avoidance procedure is based on the non-parametric mean-shift algorithm which shifts the LK integration window away from local sharp edges. Our method also locally regularizes motion by performing a fusion of local motion estimates. The regularization is made with a covariance filter which minimizes the effect of uncertainties due in part to noise and/or lack of texture. Our method is compared with other edge-preserving methods on image sequences representing different challenges. |
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