An efficiency improved recognition algorithm for highly overlapping ellipses: Application to dense bubbly flows

Langlard, Mathieu de; Al-Saddik, Hania; Charton, Sophie; Debayle, Johan; Lamadie, Fabrice

doi:10.1016/j.patrec.2017.11.024

Cited by 26 publications

(23 citation statements)

References 23 publications

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“…The first one is aimed at generalizing the model to non-spherical particles, in particular to ellipsoids frequently encountered in bubbly flows (de Langlard et al, 2018). The difficulty lies in the construction of the initial solution, as the relationships between the 2D and 3D geometrical characteristics for random ellipsoids are not straightforward.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A 3d Stochastic Model for Geometrical Characterization of Particles in Two-Phase Flow Applications

Langlard

Lamadie²,

Charton³

et al. 2018

Image Anal Stereol

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In this paper a new approach to geometrically model and characterize 2D silhouette images of two-phase flows is proposed. The method consists of a 3D modeling of the particles population based on some morphological and interaction assumptions. It includes the following steps. First, the main analytical properties of the proposed model – which is an adaptation of the Matérn type II model – are assessed, namely the effect of the thinning procedures on the population’s fundamental properties. Then, orthogonal projections of the model realizations are made to obtain 2D modeled images. The inference technique we propose and implement to determine the model parameters is a two-step numerical procedure: after a first guess of the parameters is defined, an optimization procedure is achieved to find the local minimum closest to the constructed initial solution. The method was validated on synthetic images, which has highlighted the efficiency of the proposed calibration procedure. Finally, the model was used to analyze real, i.e., experimentally acquired, silhouette images of calibrated polymethyl methacrylate (PMMA) particles. The population properties are correctly evaluated, even when suspensions of concentrated monodispersed and bidispersed particles are considered, hence highlighting the method’s relevance to describe the typical configurations encountered in bubbly flows and emulsions.

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Section: Resultsmentioning

confidence: 99%

“…The situation is even worse when other bodies, whose projections may nontrivially overlap in the image, are added (Baddeley and Jensen , 2004). This is typically the case for twophase flow images (de Langlard et al, 2018) for which classical stereological or image analysis methods fail to retrieve 3D geometrical characteristics.…”

Section: Introductionmentioning

confidence: 99%

A 3d Stochastic Model for Geometrical Characterization of Particles in Two-Phase Flow Applications

Langlard

Lamadie²,

Charton³

et al. 2018

Image Anal Stereol

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“…Regardless of the application, the modeled ellipsoidal objects are usually observed only through sections (Launeau and Robin 2005;Sahagian and Proussevitch 1998) or projections (de Langlard et al 2018a). In this paper, the focus is on orthogonal projection, sometimes called silhouette projection (Karl et al 1994), for which solely the elliptical shadow resulting from the orthographic projection is observed.…”

Section: Introductionmentioning

confidence: 99%

“…However, this type of reconstruction methods can hardly be implemented for a population of ellipsoids for which various projections at different angles are observed. It then becomes necessary to match the projections with their original ellipsoids, which may prove difficult or even impossible in some applications (de Langlard et al 2018a). The proposed method aims at addressing such situation by adopting a probabilistic approach.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inference of a Parametric Random Spheroid from its Orthogonal Projections

Langlard

Lamadie

Charton

et al. 2020

Methodol Comput Appl Probab

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The paper focuses on a new method for the inference of a parametric random spheroid from the observations of its 2D orthogonal projections. Such a stereological problem is well-known from the literature when the projections come from only one deterministic spheroid. Nevertheless, when the spheroid is random itself, the estimation of its distribution is not straightforward. From a theoretical viewpoint, it is shown that the semi-axes of the spheroid and the ones of the projected ellipses are linked through a random polynomial of degree two which admits two real random positive roots. The likelihood can be formulated in terms of the coefficients of the random polynomial, but is not analytically tractable. Assuming that the random spheroid is parameterized by a set of parameters θ real , an approximation of the maximum a posteriori is used to estimate θ real. The estimator is based on the so-called approximate Bayesian computation method and a kernel density technique. As an illustration, the case of a spheroids population, whose semi-major axis follows a gamma distribution and the flattening coefficient a truncated normal distribution, is studied. The numerical results demonstrate that the bias of the estimator is very low, with a reasonable variance, both for the first and the second order moments of the semi-axes. The proposed method enables to recover some 3D morphological characteristics of a population of independent and

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“…Moreover, bubbly flows were continuously studied by Yucheng et al [8,32], Chakraborty [33], Lau et al [3], and Tayler et al [5] from the last decade. Prolonged observations in dense bubbly flows containing overlapped ellipse-like bubbles through image analysis using algorithms were achieved by de Langlard et al [34], Honkanen et al [35,36], and Zhang et al [37], respectively.…”

Section: Introductionmentioning

confidence: 99%

A Novel One-Camera-Five-Mirror Three-Dimensional Imaging Method for Reconstructing the Cavitation Bubble Cluster in a Water Hydraulic Valve

Wang

Pooneeth

et al. 2018

Applied Sciences

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In order to study the bubble morphology, a novel experimental and numerical approach was implemented in this research focusing on the analysis of a transparent throttle valve made by Polymethylmethacrylate (PMMA) material. A feature-based algorithm was written using the MATLAB software, allowing the 2D detection and three-dimensional (3D) reconstruction of bubbles: collapsing and clustered ones. The valve core, being an important part of the throttle valve, was exposed to cavitation; hence, to distinguish it from the captured frames, the faster region-based convolutional neural network (R-CNN) algorithm was used to detect its morphology. Additionally, the main approach grouping the above listed techniques was implemented using an optimized virtual stereo vision arrangement of one camera and five plane mirrors. The results obtained during this study validated the robust algorithms and optimization applied.

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An efficiency improved recognition algorithm for highly overlapping ellipses: Application to dense bubbly flows

Cited by 26 publications

References 23 publications

A 3d Stochastic Model for Geometrical Characterization of Particles in Two-Phase Flow Applications

A 3d Stochastic Model for Geometrical Characterization of Particles in Two-Phase Flow Applications

Bayesian Inference of a Parametric Random Spheroid from its Orthogonal Projections

A Novel One-Camera-Five-Mirror Three-Dimensional Imaging Method for Reconstructing the Cavitation Bubble Cluster in a Water Hydraulic Valve

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