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Optical flow and scene flow estimation: A survey
Pattern Recognition, 2021Abstract Motion analysis is one of the most fundamental and challenging problems in the field of computer vision, which can be widely applied in many areas, such as autonomous driving, action recognition, scene understanding, and robotics. In general, the displacement field between subsequent frames can be divided into two types: optical flow and ...
Mingliang Zhai +3 more
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Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2002
We address the problem of fluid motion estimation in image sequences. For such motions, standard optical flow methods, based on intensity conservation and spatial coherence of motion field, are not suitable. This is due to the highly deformable nature of a fluid medium.
Thomas Corpetti +2 more
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We address the problem of fluid motion estimation in image sequences. For such motions, standard optical flow methods, based on intensity conservation and spatial coherence of motion field, are not suitable. This is due to the highly deformable nature of a fluid medium.
Thomas Corpetti +2 more
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Robust optical flow estimation
Proceedings of 1st International Conference on Image Processing, 2002The paper presents a robust algorithm for computation of optical flow using the principle of conservation of a set of semi-invariant local features that are representatives of local gray-level properties in an image. Specifically, a set of rotation-invariant local orthogonal Zernike moments is used as features.
Sugata Ghosal, Rajiv Mehrotra
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Wavelet-based optical flow estimation
IEEE Transactions on Circuits and Systems for Video Technology, 2002In this paper, a new algorithm for accurate optical flow estimation using discrete wavelet approximation is proposed. The proposed method takes advantages of the nature of wavelet theory, which can efficiently and accurately represent "things", to model optical flow vectors and image related functions.
Li-Fen Chen +2 more
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Optical flow estimation in aerated flows
Journal of Hydraulic Research, 2016ABSTRACTOptical flow estimation is known from Computer Vision where it is used to determine obstacle movements through a sequence of images following an assumption of brightness conservation. This paper presents the first study on application of the optical flow method to aerated stepped spillway flows.
Bung, Daniel Bernhard (PD Prof. Dr.-Ing. habil.) +1 more
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Region-based optical flow estimation
Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003A correspondence method is developed for determining optical flow where the primitive motion tokens to be matched between consecutive time frames are regions. The computation of optical flow consists of three stages: region extraction, region matching, and optical flow smoothing. The computation is completed by smoothing the initial optical flow, where
Chiou-Shann Fuh, Petros Maragos
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Simultaneous multiple optical flow estimation
[1990] Proceedings. 10th International Conference on Pattern Recognition, 2002The authors propose a simultaneous closed-form estimation method for multiple optical flow from image sequences in which each image point has multiple motions. This method only requires convolution for space-time filtering and low-dimensional eigensystem analysis as an optimization process.
Masahiko Shizawa, Kenji Mase
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A MRF approach to optical flow estimation
Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003A Markov random field (MRF) formulation for the problem of optical flow computation is studied. An adaptive window matching scheme is used to obtain a good measure of the correlation between the two images. A confidence measure for each match is also used. Thus, the input to the system is the adaptive correlation and the corresponding confidence.
John A. Vlontzos, Davi Geiger
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2005
This chapter provides a tutorial introduction to gradient-based optical flow estimation. We discuss least-squares and robust estimators, iterative coarse-to-fine refinement, different forms of parametric motion models, different conservation assumptions, probabilistic formulations, and robust mixture models.
D. Fleet, Y. Weiss
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This chapter provides a tutorial introduction to gradient-based optical flow estimation. We discuss least-squares and robust estimators, iterative coarse-to-fine refinement, different forms of parametric motion models, different conservation assumptions, probabilistic formulations, and robust mixture models.
D. Fleet, Y. Weiss
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2013
Optical flow is the velocity vector field of the projected environmental surfaces when a viewing system moves relative to the environment.
Amar Mitiche, J.K Aggarwal
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Optical flow is the velocity vector field of the projected environmental surfaces when a viewing system moves relative to the environment.
Amar Mitiche, J.K Aggarwal
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