Bayesian Estimation of Turbulent Motion

E, Patrick H #x; As,; Herzet, dric; E, Etienne M #x; Min, MIN; Heitz, Dominique; Mininni, Pablo D.

doi:10.1109/tpami.2012.232

Cited by 22 publications

(40 citation statements)

References 33 publications

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“…We plot AAE and RSME errors for both particle (above) and scalar (below) sequences. Error values are taken from Héas et al (2012). For scalar images, the proposed estimation outperforms other state-of-the-art methods in optical flow community.…”

Section: -Comparison With Other Methodsmentioning

confidence: 99%

Optical flow for incompressible turbulence motion estimation

et al. 2015

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Section: -Comparison With Other Methodsmentioning

confidence: 99%

Optical flow for incompressible turbulence motion estimation

et al. 2015

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“…In nonphysics based models, the fundamental constraints [5], [37]- [39], [46], [50] are based on an image brightness constancy constraint (IBC) [47], [48] with a smooth motion constraint, i.e., the classical Horn and Schunck (HS) method [37], [38]. It is known that such constraints are very weak against ambient changes such as illumination and nonrigid objects such as DT.…”

Section: A Non-physics Based Optical Flow Modelsmentioning

confidence: 99%

“…Using an experimental room, an IBC based on a cross-correlation method was estimated using an optic reflection model [15], [21], [40]. State-of-the-art optical flow models [38], [39], [48] use hyperparameter estimation based on stochastic models such as Ito's formula [38] and Bayesian inference [39], [48]. However, hyperparameters are globally estimated under an IBC assumption, which may prevent their application to the localized surface and texture changes created by image inhomogeneity.…”

Section: A Non-physics Based Optical Flow Modelsmentioning

confidence: 99%

“…Most previous optical flow models pose smoothing based constraints on image intensity and/or velocity, and then solve for the objective function to be minimized with global model parameter estimation/tuning. In particular, in order to capture an underlying physical property, i.e., natural phenomena, of moving scenes/objects, physics based models such as fluid dynamics (FD) [7], [8], [38], [39], [41], [48], [52] have been applied: examples include continuity equations, a simplified Navier-Stokes (NS) equation [41], and divergence-curl velocity (DC) [17]. As used in FD, the fluidity of images can be roughly quantized by the ratio of inertial force to viscosity force, which is defined in terms of low Reynolds number (Re) and/or incompressibility.…”

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confidence: 99%

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Motion Estimation for Dynamic Texture Videos Based on Locally and Globally Varying Models

Sakaino

2015

IEEE Trans. on Image Process.

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Motion estimation, i.e., optical flow, of fluid-like and dynamic texture (DT) images/videos is an important challenge, particularly for understanding outdoor scene changes created by objects and/or natural phenomena. Most optical flow models use smoothness-based constraints using terms such as fluidity from the fluid dynamics framework, with constraints typically being incompressibility and low Reynolds numbers (Re ). Such constraints are assumed to impede the clear capture of locally abrupt image intensity and motion changes, i.e., discontinuities and/or high Re over time. This paper exploits novel physics-based optical flow models/constraints for both smooth and discontinuous changes using a wave generation theory that imposes no constraint on Re or compressibility of an image sequence. Iterated two-step optimization between local and global optimization is also used: first, an objective function with varying multiple sine/cosine bases with new local image properties, i.e., orientation and frequency, and with a novel transformed dispersion relationship equation are used. Second, the statistical property of image features is used to globally optimize model parameters. Experiments on synthetic and real DT image sequences with smooth and discontinuous motions demonstrate that the proposed locally and globally varying models outperform the previous optical flow models.

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“…Traditional PIV algorithms are based on correlating the particles between image patches [1]. Recently, optical flow algorithms were used with PIV images [19,20], and different regularization terms were proposed to adapt the optical flow methods to track fluids rather than solid objects [13].…”

Section: Related Workmentioning

confidence: 99%

Refraction Wiggles for Measuring Fluid Depth and Velocity from Video

Xue¹,

Rubinstein

Wadhwa³

et al. 2014

Computer Vision – ECCV 2014

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Abstract. We present principled algorithms for measuring the velocity and 3D location of refractive fluids, such as hot air or gas, from natural videos with textured backgrounds. Our main observation is that intensity variations related to movements of refractive fluid elements, as observed by one or more video cameras, are consistent over small space-time volumes. We call these intensity variations "refraction wiggles", and use them as features for tracking and stereo fusion to recover the fluid motion and depth from video sequences. We give algorithms for 1) measuring the (2D, projected) motion of refractive fluids in monocular videos, and 2) recovering the 3D position of points on the fluid from stereo cameras. Unlike pixel intensities, wiggles can be extremely subtle and cannot be known with the same level of confidence for all pixels, depending on factors such as background texture and physical properties of the fluid. We thus carefully model uncertainty in our algorithms for robust estimation of fluid motion and depth. We show results on controlled sequences, synthetic simulations, and natural videos. Different from previous approaches for measuring refractive flow, our methods operate directly on videos captured with ordinary cameras, do not require auxiliary sensors, light sources or designed backgrounds, and can correctly detect the motion and location of refractive fluids even when they are invisible to the naked eye.

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Bayesian Estimation of Turbulent Motion

Cited by 22 publications

References 33 publications

Optical flow for incompressible turbulence motion estimation

Optical flow for incompressible turbulence motion estimation

Motion Estimation for Dynamic Texture Videos Based on Locally and Globally Varying Models

Refraction Wiggles for Measuring Fluid Depth and Velocity from Video

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