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WO2011138766A1 - Système et procédé permettant de mesurer à distance la carte de vitesse directionnelle d'émissions gazeuses - Google Patents

Système et procédé permettant de mesurer à distance la carte de vitesse directionnelle d'émissions gazeuses Download PDF

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Publication number
WO2011138766A1
WO2011138766A1 PCT/IB2011/052018 IB2011052018W WO2011138766A1 WO 2011138766 A1 WO2011138766 A1 WO 2011138766A1 IB 2011052018 W IB2011052018 W IB 2011052018W WO 2011138766 A1 WO2011138766 A1 WO 2011138766A1
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WIPO (PCT)
Prior art keywords
correlation coefficient
temporal sequence
images
pixels
coefficient function
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Ceased
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PCT/IB2011/052018
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English (en)
Inventor
Simon Savary
Pierre Tremblay
André VILLEMAIRE
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Telops Inc
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Telops Inc
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Publication of WO2011138766A1 publication Critical patent/WO2011138766A1/fr
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P5/00—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft
    • G01P5/18—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft by measuring the time taken to traverse a fixed distance
    • G01P5/22—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft by measuring the time taken to traverse a fixed distance using auto-correlation or cross-correlation detection means

Definitions

  • the method uses a time sequence of consecutive images of a given scene exhibiting a gas plume.
  • the camera may operate in any spectral band, i.e. ultraviolet, visible or infrared, as long as it provides contrasted images of the gas.
  • the method takes benefit from the similarity in the time sequence variations resulting from gas movements in order to localize the gas molecules in space and time. Directional velocity components are thus inferred from the latter information.
  • a method and a system to remotely measure the directional velocity of gas plumes from time sequence of camera images comprises acquiring a time sequence of scene images showing contrasted gas plume information; calculating time sequence similarity to localize the gas movement in space and time; determining the directional velocity components, both horizontal and vertical.
  • a method for remotely measuring a directional velocity of a gas plume in a scene comprises providing a temporal sequence of images, the scene showing contrasted gas plume information for the gas plume, the images being acquired using a camera; calculating temporal sequence similarity on the temporal sequence of images, the similarity being a similarity in variations resulting from movement of the gas plume; determining a directional velocity including horizontal and vertical directional velocity components from the temporal sequence similarity.
  • the method further comprises providing a filtering criterion, identifying and eliminating pixels in the temporal sequence of images for which the filtering criterion is met.
  • calculating temporal sequence similarity on the temporal sequence of images includes calculating temporal variations of intensities of individual pixels of the images. [0011] In one embodiment, the method further comprises calculating a correlation coefficient function between at least a subset of pixels of the temporal sequence.
  • the method further comprises providing a correlation coefficient criterion for an unfit correlation coefficient function and identifying and eliminating pixels for which the calculated correlation coefficient function meets the criterion.
  • the method further comprises obtaining an integer shift at which the maximum of the correlation coefficient function occurs. [0017] In one embodiment, the method further comprises determining a subsample peak location of the maximum of the correlation coefficient function using a quadratic peak interpolation technique, the correlation coefficient function and the integer shift.
  • the method further comprises providing a threshold value for the maximum of the correlation coefficient function and identifying and eliminating the maximum of the correlation coefficient function which is smaller than the threshold value and one of the integer shift and the subsample peak location of the eliminated maximum.
  • the camera is an imaging Fourier-transform spectrometer, wherein the temporal sequence is an interferogram, further comprising filtering the interferogram prior to the calculating temporal sequence similarity.
  • FIG. 2 shows a flow chart of an example embodiment of the method
  • Fig. 3 is an exploded view of step 41 of Fig. 2 which shows the decision paths when the correlation search matrices are updated;
  • Fig. 4 is a photograph and shows an example of the 2-dimensional velocity map, for an image having 200 x 200 pixels, obtained when using an example embodiment of the method;
  • Fig. 5 is a photograph and shows an example of the 2-dimensional velocity map, for a partial image having 90 x 100 pixels, obtained when using an example embodiment of the method.
  • the proposed method performs a remote and passive analysis and only requires acquisition of sequences that contain contrasted gas flows. Instead of using strictly spatial correlation like most methods do, the present method uses the temporal variations of the intensities of individual pixels. This allows tracking of turbulent fluids and of relatively slow evolving shapes, as well as rigidly shaped bodies.
  • the method uses temporal sequences of images taken by a camera when looking at a given scene.
  • the camera may operate in any spectral band, i.e. ultraviolet, visible or infrared, providing contrasted images of the scene. No assumption is made on the initial shape of the moving object.
  • Figure 1 was formed from a 5 x 5 subset of a real measurement sequence and represents a sliding subwindow used for an example embodiment of the method.
  • the pixel at the center of the subwindow is referred to as the reference pixel.
  • the method makes use of temporal sequences of images, it produces estimates of each localized directional velocity averaged over the observation time.
  • Figure 2 depicts a flowchart of the example steps for an example embodiment of the method 20.
  • the first branch at left, describes the preparation of the data
  • the second and third branches form two nested loops.
  • the outer loop identifies all pixels of interest (reference pixels) and computes the velocity estimates.
  • the inner loop computes the cross-correlations between the reference pixel and the neighboring pixels included in the correlation search window.
  • the quantification of the similarity between temporal sequences observed from different pixels of the image is first obtained. For most applications, the contrast in the images is sufficient when the contribution to the statistical distribution of one pixel due to fluctuations in the scene is greater than that of the other signals (e.g. noise, interference).
  • ⁇ r ref is the variance estimate of the sequence of the reference pixel
  • the similarity characterization procedure yields a pair of correlation search matrices for every reference pixel, labeled P and K , defined in Equation 8 (step 41).
  • the reference pixel is labeled (0,0) in this definition.
  • the correlation entries in submatrix ⁇ ( ⁇ , Aj) can be forced to zero when a cross-correlation is determined invalid, thus allowing exclusion or fixing of unfit situations. For example, bad detector pixels would generate unpredictable results. When a map of these bad detector pixels exists, it can be used to exclude them from the analysis by setting entry ⁇ ( ⁇ , ⁇ ) to 0 (steps 53 and 54).
  • the computing time is further improved by restricting k ⁇ to a small initial value.
  • factor can be 0.1.
  • the best correlation shift k 0 reaches k ⁇ for a correlation sequence, the latter is augmented by a factor S , because the true k 0 occurred beyond k ⁇ .
  • factor ⁇ can be 4 (step 34). This extension procedure is applied until
  • ⁇ k ⁇ or k ⁇ N , whichever occurs first. The decision is taken by the branch of step 33.
  • Equation 2 may be calculated by using a Fast Fourier Transform algorithm. Such a technique enables faster evaluation of k 0 .
  • the cross-correlation is given in the Fourier transform domain by Equation 9.
  • the correlation search matrices ⁇ ( ⁇ , ⁇ / ' ) and K(Ai, Aj) are used as a basis for computing the average velocity vectors for every pixel.
  • Matrix ⁇ ( ⁇ , ⁇ / ' ) gives the similarity measure of each surrounding pixel.
  • Thresholding matrix ⁇ ( ⁇ , ⁇ / ' ) entries allow for the selection of a subset of valid values in ⁇ ( ⁇ , Aj) .
  • a threshold value may be defined to consider that the estimated correlation coefficient is sufficiently large to infer that the velocity estimate is declared valid.
  • the method can work satisfactorily well with a threshold of 0 for the correlation value, i.e. a correlation shift in K(Ai, Aj) is considered valid as long as its corresponding correlation coefficient in ⁇ ( ⁇ , ⁇ ) is strictly positive (greater than zero).
  • a negative correlation coefficient would indicate anti-correlation (inverted shape), which is not desired in some contexts.
  • Step 43 in Figure 2 implements the valid pixel selection.
  • the sequence of images taken by the camera has space-time parameters, which are the horizontal pixel spacing ⁇ , the vertical pixel spacing Ay and the time interval between successive images At .
  • the latter is the inverse of the sampling frequency or image frame rate.
  • the pixel spacing is obtained from the distance d separating the scene and the recording instrument and the angular instantaneous field of view (iFOV) of the instrument.
  • ⁇ and Ay relate to d and the iFOV through Equation 10.
  • a speed estimate matrix is computed from the correlation search matrix (step 44).
  • the shifts k with correlations coefficients higher than the chosen correlation threshold are used to compute the speed estimates.
  • Its horizontal and vertical components (v ref x , v ref ) are obtained by computing the ratio of the distances between the subwindow neighbor pixels to the reference pixel and the time shifts showing the larger correlation. It is presented in Equation 1 1.
  • the embodiments can be provided by combinations of hardware and software components, with some components being implemented by a given function or operation of a hardware or software system, and many of the data paths illustrated being implemented by data communication within a computer application or operating system or can be communicatively linked using any suitable known or after-developed wired and/or wireless methods and devices. Sensors, processors and other devices can be co-located or remote from one or more of each other. The structure illustrated is thus provided for efficiency of teaching the embodiments.

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  • Engineering & Computer Science (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Image Analysis (AREA)

Abstract

L'invention concerne un procédé et un système permettant de mesurer à distance la vitesse directionnelle de panaches de gaz à partir d'une séquence temporelle d'images de caméra. Le procédé selon l'invention consiste : à acquérir une séquence temporelle d'images de scène présentant des informations de panaches de gaz contrastées ; à calculer une similarité de séquence temporelle pour localiser le mouvement des gaz dans l'espace et dans le temps ; et à déterminer les composantes de vitesse directionnelle, à la fois horizontales et verticales.
PCT/IB2011/052018 2010-05-07 2011-05-06 Système et procédé permettant de mesurer à distance la carte de vitesse directionnelle d'émissions gazeuses Ceased WO2011138766A1 (fr)

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US61/332,260 2010-05-07

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9442011B2 (en) 2014-06-23 2016-09-13 Exxonmobil Upstream Research Company Methods for calibrating a multiple detector system
US9448134B2 (en) 2014-06-23 2016-09-20 Exxonmobil Upstream Research Company Systems for detecting a chemical species and use thereof
US9471969B2 (en) 2014-06-23 2016-10-18 Exxonmobil Upstream Research Company Methods for differential image quality enhancement for a multiple detector system, systems and use thereof
US9501827B2 (en) 2014-06-23 2016-11-22 Exxonmobil Upstream Research Company Methods and systems for detecting a chemical species
US20200092566A1 (en) * 2018-09-19 2020-03-19 Power Diagnostic Technologies Ltd. Method and system for determining a flow rate of a fugitive fluid plume
US11519602B2 (en) 2019-06-07 2022-12-06 Honeywell International Inc. Processes and systems for analyzing images of a flare burner

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH04291160A (ja) * 1991-03-20 1992-10-15 Yaskawa Electric Corp 流速測定方法およびその装置
US5249238A (en) * 1991-03-19 1993-09-28 Komerath Narayanan M Spatial cross-correlating velocimeter
WO2004005941A1 (fr) * 2002-07-05 2004-01-15 Stichting Voor De Technische Wetenschappen Procede de correlation d'un ensemble a deux points pour des applications de dynamique des fluides

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5249238A (en) * 1991-03-19 1993-09-28 Komerath Narayanan M Spatial cross-correlating velocimeter
JPH04291160A (ja) * 1991-03-20 1992-10-15 Yaskawa Electric Corp 流速測定方法およびその装置
WO2004005941A1 (fr) * 2002-07-05 2004-01-15 Stichting Voor De Technische Wetenschappen Procede de correlation d'un ensemble a deux points pour des applications de dynamique des fluides

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9442011B2 (en) 2014-06-23 2016-09-13 Exxonmobil Upstream Research Company Methods for calibrating a multiple detector system
US9448134B2 (en) 2014-06-23 2016-09-20 Exxonmobil Upstream Research Company Systems for detecting a chemical species and use thereof
US9471969B2 (en) 2014-06-23 2016-10-18 Exxonmobil Upstream Research Company Methods for differential image quality enhancement for a multiple detector system, systems and use thereof
US9501827B2 (en) 2014-06-23 2016-11-22 Exxonmobil Upstream Research Company Methods and systems for detecting a chemical species
US9760995B2 (en) 2014-06-23 2017-09-12 Exxonmobil Upstream Research Company Methods and systems for detecting a chemical species
US20200092566A1 (en) * 2018-09-19 2020-03-19 Power Diagnostic Technologies Ltd. Method and system for determining a flow rate of a fugitive fluid plume
US11519602B2 (en) 2019-06-07 2022-12-06 Honeywell International Inc. Processes and systems for analyzing images of a flare burner

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