Computation of Fluid and Particle Motion From a Time-Sequenced Image Pair: A Global Outlier Identification Approach

Ray, Nilanjan

doi:10.1109/tip.2011.2142005

Cited by 9 publications

(3 citation statements)

References 35 publications

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“…In fact, (HS) method used here is exactly the same as SGSD but does not include the turbulent diffusion term. A PIV-based method (Ray, 2011) that is capable of producing dense vector fields (necessary for trajectory reconstruction) is also compared to the two previous methods.…”

Section: Discussionmentioning

confidence: 99%

The development of image processing technique to study the interfacial behavior of air-water slug two-phase flow in horizontal pipes

Widyatama¹,

Dinaryanto²,

Indarto³

et al. 2018

Flow Measurement and Instrumentation

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We present a novel motion estimation technique for image-based river velocimetry. It is based on the so-called optical flow, which is a well developed method for rigid motion estimation in image sequences, devised in computer vision community. Contrary to PIV (Particle Image Velocimetry) techniques, optical flow formulation is flexible enough to incorporate physics equations that govern the observed quantity motion. Over the past years, it has been adopted by experimental fluid dynamics community where many new models were introduced to better represent different fluids motions, (see (Heitz et al., 2010) for a review). Our optical flow is based on the scalar transport equation and is augmented with a weighted diffusion term to compensate for small scale (non-captured) contributions. Additionally, since there is no ground truth data for such type of image sequences, we present a new evaluation method to assess the results. It is based on trajectory reconstruction of few Lagrangian particles of interest and a direct comparison against their manually-reconstructed trajectories. The new motion estimation technique outperformed traditional optical flow and PIV-based methods.

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Section: Discussionmentioning

confidence: 99%

The development of image processing technique to study the interfacial behavior of air-water slug two-phase flow in horizontal pipes

Widyatama¹,

Dinaryanto²,

Indarto³

et al. 2018

Flow Measurement and Instrumentation

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show abstract

“…For the sake of simplicity, the optimization process is shown for one resolution framework. The proposed architecture implements the general principle of the multiresolution framework, where computations follow coarse-to-fine resolutions [38]. It has three resolutions/scales denoted by a quarter g…”

Section: A Generative Multiresolution Convolutional Network (Gmcnet)mentioning

confidence: 99%

GMCNet: A Generative Multi-Resolution Framework for Cardiac Registration

et al. 2023

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Deformable image registration plays a crucial role in estimating cardiac deformation from a sequence of images. However, existing registration methods primarily process images as pairs instead of processing all images in a sequence together. This study proposes a novel end-to-end learning-free generative multi-resolution convolutional neural network (GMCNet) with the primary focus of registering images in a sequence. Even though learning-based methods have yielded high performance for image registration, their performance depends on their ability to learn information from a large number of samples which are difficult to obtain and might bias the framework to the specific domain of data. The proposed learning-free method eliminates the need for a dedicated training set while exploiting the capabilities of neural networks to achieve accurate deformation fields. Due to its capability of parameter sharing through the architecture, the GMCNet can be used as a groupwise registration as well as pairwise registration. The proposed method was evaluated on three different clinical cardiac magnetic resonance imaging datasets and compared quantitatively against nine other state-of-the-art learning and optimization-based algorithms. The proposed method outperformed other methods in all comparisons and yielded average Dice metric values ranging from 0.85 to 0.88 for the datasets. Different aspects of the GMCNet are also explored by assessing 1) the robustness; 2) performance on pairwise registration; 3) the influence of spatial transformation in a controlled environment; and 4) the impact of different multi-resolution structures. The results demonstrate that using temporal information to estimate the deformation fields leads to more accurate registration results and improved robustness under different noise levels. Moreover, the proposed method does not need images for training, and therefore, its prediction is not domain-specific and can be applied to any sequence of images.

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“…Velocity estimation has been remarkably improved over the past years [11][12][13][14][15]. Estimation of fluid motion has unique difficulties because of its special characteristics.…”

Section: Related Workmentioning

confidence: 99%

Fluid re-simulation based on physically driven model from video

2015

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Customizing a desired naturalistic fluid simulation result from video to obtain similar artistic effect is significant in practice. But art-directed customizing is challengeable due to the chaotic nature of the physics contained in it, and this still remains to be a difficult task in spite of rapid advancements of computer graphics during the last two decades. This paper focuses on the problem of physically based fluid re-simulation which is foundational to customize desired naturalistic simulation result from video example. In the previous achievements, conventional algorithms primarily recover 3D geometry of fluid surface or obtain the velocity of fluid particles in video. However, to launch new derivative results, just geometry and velocity are not enough, and physically based driven models are promising. We present a novel method that is capable of efficiently recovering physically driven model from an existing video. We advocate a new approach to calculate the velocity and non-normalized surface geometry under the constraints of the appearance and dynamic behavior of the example fluid in multi-scale framework, and use the calculated physical properties quickly to recover the fluid-driven model. In particular, to calculate the surface geometry more accurately, we introduce a scale factor between normalized geometry and non-normalized geometry to acquire a more accurate result. We propose a novel recovery algorithm, in which the particle densities of lattice Boltzmann method can be recovered more accurately from matching fluid advection geometry with the calculated non-normalized geometry. Fluid re-simulations with different types of density can be achieved, including constrained particle density, auto-advection density, and enhanced particle density. We demonstrate our results in several challenging scenarios and provide qualitative evaluation to our method. Some applications based on our approach are also demonstrated in the implementation.

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Computation of Fluid and Particle Motion From a Time-Sequenced Image Pair: A Global Outlier Identification Approach

Cited by 9 publications

References 35 publications

The development of image processing technique to study the interfacial behavior of air-water slug two-phase flow in horizontal pipes

The development of image processing technique to study the interfacial behavior of air-water slug two-phase flow in horizontal pipes

GMCNet: A Generative Multi-Resolution Framework for Cardiac Registration

Fluid re-simulation based on physically driven model from video

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