Optical flow estimation for motion-compensated compression

Chen, Wei; Mied, Richard P.

doi:10.1016/j.imavis.2013.01.002

Cited by 19 publications

(9 citation statements)

References 26 publications

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“…Implementation details and algorithms of the iteration approaches are very similar to the methods reported in Chen [2011, ], and Chen and Mied [].…”

Section: Nonlinear Multiple‐tracer Modelmentioning

confidence: 99%

“…An added benefit is that pixel‐to‐pixel noise is also diminished greatly and avoids numerical problems in inverting the matrices for a solution. We then use a generic bilinear function [ Chen , 2011, ; Chen and Mied , ] to interpolate values of the spatial tracer gradients at any position ( x , y ) = ( i + u ij Δ t , j + v ij Δ t ) between pixels in the scene.…”

Section: Nonlinear Multiple‐tracer Modelmentioning

confidence: 99%

“…In these areas, the motion field solution is not unique, since the displacement vectors cannot be easily determined in these ambiguous regions. An iteration algorithm of the Progressive Relaxation of the Over‐Constraint (PROC) type proposed by Chen [2011, ] and Chen and Mied [] is a successful approach for finding a solution in which each vector is consistent with its neighbors, and we employ it here.…”

Section: Nonlinear Multiple‐tracer Modelmentioning

confidence: 99%

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River velocities from sequential multispectral remote sensing images

Chen

Mied

2013

Water Resour. Res.

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[1] We address the problem of extracting surface velocities from a pair of multispectral remote sensing images over rivers using a new nonlinear multiple-tracer form of the global optimal solution (GOS). The derived velocity field is a valid solution across the image domain to the nonlinear system of equations obtained by minimizing a cost function inferred from the conservation constraint equations for multiple tracers. This is done by deriving an iteration equation for the velocity, based on the multiple-tracer displaced frame difference equations, and a local approximation to the velocity field. The number of velocity equations is greater than the number of velocity components, and thus overly constrain the solution. The iterative technique uses Gauss-Newton and LevenbergMarquardt methods and our own algorithm of the progressive relaxation of the overconstraint. We demonstrate the nonlinear multiple-tracer GOS technique with sequential multispectral Landsat and ASTER images over a portion of the Potomac River in MD/VA, and derive a dense field of accurate velocity vectors. We compare the GOS river velocities with those from over 12 years of data at four NOAA reference stations, and find good agreement. We discuss how to find the appropriate spatial and temporal resolutions to allow optimization of the technique for specific rivers.Citation: Chen, W., and R. P. Mied (2013), River velocities from sequential multispectral remote sensing images, Water Resour. Res., 49,[3093][3094][3095][3096][3097][3098][3099][3100][3101][3102][3103]

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“…Implementation details and algorithms of the iteration approaches are very similar to the methods reported in Chen [2011, ], and Chen and Mied [].…”

Section: Nonlinear Multiple‐tracer Modelmentioning

confidence: 99%

Section: Nonlinear Multiple‐tracer Modelmentioning

confidence: 99%

Section: Nonlinear Multiple‐tracer Modelmentioning

confidence: 99%

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River velocities from sequential multispectral remote sensing images

Chen

Mied

2013

Water Resour. Res.

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“…This algorithm is different from optical flow method which is also based on Taylor expansion. In optical flow method [14,15], the velocities and directions of all pixels, that is, the optical flow field, are estimated by using the variation of pixels in image sequence in the time domain and the correlation between adjacent frames to find the correspondence between two frames. The algorithm proposed in this paper is based on the assumption of rigid motion, and the relative displacement between frames is calculated by numerical iteration.…”

Section: (5)mentioning

confidence: 99%

In Situ Measurement of Wind-Induced Pulse Response of Sound Barrier Based on High-Speed Imaging Technology

Zhu

Guo

Zhang

et al. 2016

Mathematical Problems in Engineering

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The lifetime of the sound barrier is threatened by high-speed train-induced impulsive wind pressure as it passes by. The vibration response of the sound barrier during the process of train passing is difficult to be measured using conventional measurement methods because of the inconvenience of the installation of markers on the sound barrier. In this paper, the high-speed camera is used to record the whole process of the train passing by the sound barrier. Then, a displacement extraction algorithm based on the theory of Taylor expansion is proposed to obtain the vibration response curve. Compared with the result simulated by using the finite element method, the video extraction result shows the same head wave and tail wave phenomenon, demonstrating that the vibration measurement by using the high-speed imaging technology is an effective measuring way. It can achieve noncontact and remote vibration measurement and has important practical value.

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“…Since Horn Shunck model [37] and Lucas Kanade model [44] were proposed in 1981, optical flow has been broadly applied to various fields, such as visual tracking [33], structure from motion (SFM) [22], motion segmentation [58,66], object recognition [26], video surveillance [1,3,30,49], visual odometry (VO) [16,55], and even video compression [20].…”

Section: Introductionmentioning

confidence: 99%

Real-time one-dimensional motion estimation and its application in computer vision

Yang

Gong

Tang

et al. 2015

Machine Vision and Applications

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Traditional optical flow methods are designed to generate two-dimensional (2D) motion fields. However in some applications, only the motion field along a predefined line is needed, while the rest of the redundant 2D region (Cong et al., CVPR, 2009) can be ignored. In this paper, we propose a general framework for estimating one-dimensional (1D) motion field using the L 1 norm metric, which can be flexible for adding any constraints depending on the specific intention. Compared with 2D methods in model calculation, which often obtain a local optimal solution, our 1D version has the advantage of real-time global optimization and sub-pixel accuracy with a large displacement capacity. The synthesized experiments have justified the effectiveness of our 1D model. Two vision applications are investigated: (i) for crowd counting, we design a novel unified framework for both line of interest and region of interest counting using our 1D motion model to mosaic the dynamic blobs. Such a framework can adapt to new site without scene-specific learning and overcome overfitting. (ii) For visual odometry, we apply our 1D model to estimate the linear velocity of the robot in real time and obtain a comparable result with the benchmark Encoder. Experiments verified that our proposed model can produce satisfactory results in real time.

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Optical flow estimation for motion-compensated compression

Cited by 19 publications

References 26 publications

River velocities from sequential multispectral remote sensing images

River velocities from sequential multispectral remote sensing images

In Situ Measurement of Wind-Induced Pulse Response of Sound Barrier Based on High-Speed Imaging Technology

Real-time one-dimensional motion estimation and its application in computer vision

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