Abstract

The location of a flame front is often taken as the point of maximum OH gradient. Planar laser-induced fluorescence of OH can be used to obtain the flame front by extracting the points of maximum gradient. This operation is typically performed using an edge detection algorithm. The choice of operating parameters a priori poses significant problems of robustness when handling images with a range of signal-to-noise ratios. A statistical method of parameter selection originating in the image processing literature is detailed, and its merit for this application is demonstrated. A reduced search space method is proposed to decrease computational cost and render the technique viable for large data sets. This gives nearly identical output to the full method. These methods demonstrate substantial decreases in data rejection compared to the use of a priori parameters. These methods are viable for any application where maximum gradient contours must be accurately extracted from images of species or temperature, even at very low signal-to-noise ratios.

© 2009 Optical Society of America

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References

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  1. G. Hartung, J. Hult, C. F. Kaminksi, J. W. Rogerson, and N. Swaminathan, “Effect of heat release on turbulence and scalar-turbulence interaction in premixed combustion,” Phys. Fluids 20, 035110 (2008).
    [CrossRef]
  2. E. P. Hassel and S. Linow, “Laser diagnostics for studies of turbulent combustion,” Meas. Sci. Technol. 11, R37-R57 (2000).
    [CrossRef]
  3. B. O. Ayoola, R. Balachandran, J. H. Frank, E. Mastorakos, and C. F. Kaminski, “Spatially resolved heat release rate measurements in turbulent premixed flames,” Combust. Flame 144, 1-16 (2006).
    [CrossRef]
  4. R. S. Barlow, G.-H. Wang, P. Anselmo-Filho, M. S.Sweeney, and S. Hochgreb, “Application of Raman/Rayleigh/LIF diagnostics in turbulent stratified flames,” Proc. Combust. Inst. 32, 945-953 (2008).
    [CrossRef]
  5. D. Veynante, J. Piana, J. M. Duclos, and C. Martel, “Experimental analysis of flame surface density models for premixed turbulent combustion,” Proc. Combust. Inst. 26, 413-420 (1996).
  6. S. Gashi, J. Hult, K. W. Jenkins, N. Chakraborty, S. Cant, and C. F. Kaminski, “Curvature and wrinkling of premixed flame kernels--comparisons of OH PLIF and DNS data,” Proc. Combust. Inst. 30, 809-817 (2005).
    [CrossRef]
  7. O. Nobuyuki, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man Cybern. 9, 62-66 (1979).
    [CrossRef]
  8. L. G. Roberts, “Machine perception of three dimensional solids,” in Optical and Electro-Optical Information Processing, J. Tippett, ed. (MIT Press, 1965), pp. 159-197
  9. J. M. S. Prewitt, “Object enhancement and extraction,” in Picture Processing and Psychopictorics, B. S. Lipkin and A. Rosenfeld, eds. (Academic, 1970), pp. 75-149
  10. I. Sobel, “Camera models and machine perception,” Ph.D. dissertation (Stanford University, 1970).
  11. J. F. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8, 679-698(1986).
    [CrossRef]
  12. H. Malm, G. Sparr, J. Hult, and C. F. Kaminski, “Nonlinear diffusion filtering of images obtained by planar-laser-induced fluorescence spectroscopy,” J. Opt. Soc. Am. A 17, 2148-2156 (2000).
    [CrossRef]
  13. T. Peli and D. Malah, “A study of edge detection algorithms,” Comput. Graph. Image Process. 20, 1-21 (1982).
    [CrossRef]
  14. M. Heath, S. Sarkar, T. Sanocki, and K. Bowyer, “Comparison of edge detectors: amethodology and initial study,” Comput. Vision Image Understand. 69, 38-54 (1998).
    [CrossRef]
  15. S. Venkatesh and L. J. Kitchen, “Edge evaluation using necessary components,” CVGIP: Graph. Models Image Process. 54, 23-30 (1992).
    [CrossRef]
  16. A. Hoover, G. Jean-Baptiste, X. Jiang, P. J. Flynn, H. Bunke, D. Goldof, and K. Bowyer, “Comparison of range image segmentation algorithms,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 673-689 (1996).
    [CrossRef]
  17. Y. J. Zhang, “A survey on evaluation methods for image segmentation,” Pattern Recogn. 29, 1335-1346 (1996).
    [CrossRef]
  18. R. Román, J. F. Gómez-Lopera, C. Atae-Allah, J. Martínez-Aroza, and P. L. Luque-Escamilla, “A measure of quality for evaluating methods of segmentation and edge detection,” Pattern Recogn. 34, 969-980 (2001).
    [CrossRef]
  19. Y. Yitzhaky and E. Peli, “A method for objectve edge detection evaluation and detector parameter selection,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1027-1033 (2003).
    [CrossRef]
  20. K. Raghupathy, “Curve tracing and curve detection in images,” M.S. thesis (Cornell University, 2004).
  21. H. Kraemer, Evaluating Medical Tests: Objective and Quantitative Guidelines (Sage Publications, 1992).

2008

G. Hartung, J. Hult, C. F. Kaminksi, J. W. Rogerson, and N. Swaminathan, “Effect of heat release on turbulence and scalar-turbulence interaction in premixed combustion,” Phys. Fluids 20, 035110 (2008).
[CrossRef]

R. S. Barlow, G.-H. Wang, P. Anselmo-Filho, M. S.Sweeney, and S. Hochgreb, “Application of Raman/Rayleigh/LIF diagnostics in turbulent stratified flames,” Proc. Combust. Inst. 32, 945-953 (2008).
[CrossRef]

2006

B. O. Ayoola, R. Balachandran, J. H. Frank, E. Mastorakos, and C. F. Kaminski, “Spatially resolved heat release rate measurements in turbulent premixed flames,” Combust. Flame 144, 1-16 (2006).
[CrossRef]

2005

S. Gashi, J. Hult, K. W. Jenkins, N. Chakraborty, S. Cant, and C. F. Kaminski, “Curvature and wrinkling of premixed flame kernels--comparisons of OH PLIF and DNS data,” Proc. Combust. Inst. 30, 809-817 (2005).
[CrossRef]

2003

Y. Yitzhaky and E. Peli, “A method for objectve edge detection evaluation and detector parameter selection,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1027-1033 (2003).
[CrossRef]

2001

R. Román, J. F. Gómez-Lopera, C. Atae-Allah, J. Martínez-Aroza, and P. L. Luque-Escamilla, “A measure of quality for evaluating methods of segmentation and edge detection,” Pattern Recogn. 34, 969-980 (2001).
[CrossRef]

2000

1998

M. Heath, S. Sarkar, T. Sanocki, and K. Bowyer, “Comparison of edge detectors: amethodology and initial study,” Comput. Vision Image Understand. 69, 38-54 (1998).
[CrossRef]

1996

A. Hoover, G. Jean-Baptiste, X. Jiang, P. J. Flynn, H. Bunke, D. Goldof, and K. Bowyer, “Comparison of range image segmentation algorithms,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 673-689 (1996).
[CrossRef]

Y. J. Zhang, “A survey on evaluation methods for image segmentation,” Pattern Recogn. 29, 1335-1346 (1996).
[CrossRef]

D. Veynante, J. Piana, J. M. Duclos, and C. Martel, “Experimental analysis of flame surface density models for premixed turbulent combustion,” Proc. Combust. Inst. 26, 413-420 (1996).

1992

S. Venkatesh and L. J. Kitchen, “Edge evaluation using necessary components,” CVGIP: Graph. Models Image Process. 54, 23-30 (1992).
[CrossRef]

1986

J. F. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8, 679-698(1986).
[CrossRef]

1982

T. Peli and D. Malah, “A study of edge detection algorithms,” Comput. Graph. Image Process. 20, 1-21 (1982).
[CrossRef]

1979

O. Nobuyuki, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man Cybern. 9, 62-66 (1979).
[CrossRef]

Anselmo-Filho, P.

R. S. Barlow, G.-H. Wang, P. Anselmo-Filho, M. S.Sweeney, and S. Hochgreb, “Application of Raman/Rayleigh/LIF diagnostics in turbulent stratified flames,” Proc. Combust. Inst. 32, 945-953 (2008).
[CrossRef]

Atae-Allah, C.

R. Román, J. F. Gómez-Lopera, C. Atae-Allah, J. Martínez-Aroza, and P. L. Luque-Escamilla, “A measure of quality for evaluating methods of segmentation and edge detection,” Pattern Recogn. 34, 969-980 (2001).
[CrossRef]

Ayoola, B. O.

B. O. Ayoola, R. Balachandran, J. H. Frank, E. Mastorakos, and C. F. Kaminski, “Spatially resolved heat release rate measurements in turbulent premixed flames,” Combust. Flame 144, 1-16 (2006).
[CrossRef]

Balachandran, R.

B. O. Ayoola, R. Balachandran, J. H. Frank, E. Mastorakos, and C. F. Kaminski, “Spatially resolved heat release rate measurements in turbulent premixed flames,” Combust. Flame 144, 1-16 (2006).
[CrossRef]

Barlow, R. S.

R. S. Barlow, G.-H. Wang, P. Anselmo-Filho, M. S.Sweeney, and S. Hochgreb, “Application of Raman/Rayleigh/LIF diagnostics in turbulent stratified flames,” Proc. Combust. Inst. 32, 945-953 (2008).
[CrossRef]

Bowyer, K.

M. Heath, S. Sarkar, T. Sanocki, and K. Bowyer, “Comparison of edge detectors: amethodology and initial study,” Comput. Vision Image Understand. 69, 38-54 (1998).
[CrossRef]

A. Hoover, G. Jean-Baptiste, X. Jiang, P. J. Flynn, H. Bunke, D. Goldof, and K. Bowyer, “Comparison of range image segmentation algorithms,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 673-689 (1996).
[CrossRef]

Bunke, H.

A. Hoover, G. Jean-Baptiste, X. Jiang, P. J. Flynn, H. Bunke, D. Goldof, and K. Bowyer, “Comparison of range image segmentation algorithms,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 673-689 (1996).
[CrossRef]

Canny, J. F.

J. F. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8, 679-698(1986).
[CrossRef]

Cant, S.

S. Gashi, J. Hult, K. W. Jenkins, N. Chakraborty, S. Cant, and C. F. Kaminski, “Curvature and wrinkling of premixed flame kernels--comparisons of OH PLIF and DNS data,” Proc. Combust. Inst. 30, 809-817 (2005).
[CrossRef]

Chakraborty, N.

S. Gashi, J. Hult, K. W. Jenkins, N. Chakraborty, S. Cant, and C. F. Kaminski, “Curvature and wrinkling of premixed flame kernels--comparisons of OH PLIF and DNS data,” Proc. Combust. Inst. 30, 809-817 (2005).
[CrossRef]

Duclos, J. M.

D. Veynante, J. Piana, J. M. Duclos, and C. Martel, “Experimental analysis of flame surface density models for premixed turbulent combustion,” Proc. Combust. Inst. 26, 413-420 (1996).

Flynn, P. J.

A. Hoover, G. Jean-Baptiste, X. Jiang, P. J. Flynn, H. Bunke, D. Goldof, and K. Bowyer, “Comparison of range image segmentation algorithms,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 673-689 (1996).
[CrossRef]

Frank, J. H.

B. O. Ayoola, R. Balachandran, J. H. Frank, E. Mastorakos, and C. F. Kaminski, “Spatially resolved heat release rate measurements in turbulent premixed flames,” Combust. Flame 144, 1-16 (2006).
[CrossRef]

Gashi, S.

S. Gashi, J. Hult, K. W. Jenkins, N. Chakraborty, S. Cant, and C. F. Kaminski, “Curvature and wrinkling of premixed flame kernels--comparisons of OH PLIF and DNS data,” Proc. Combust. Inst. 30, 809-817 (2005).
[CrossRef]

Goldof, D.

A. Hoover, G. Jean-Baptiste, X. Jiang, P. J. Flynn, H. Bunke, D. Goldof, and K. Bowyer, “Comparison of range image segmentation algorithms,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 673-689 (1996).
[CrossRef]

Gómez-Lopera, J. F.

R. Román, J. F. Gómez-Lopera, C. Atae-Allah, J. Martínez-Aroza, and P. L. Luque-Escamilla, “A measure of quality for evaluating methods of segmentation and edge detection,” Pattern Recogn. 34, 969-980 (2001).
[CrossRef]

Hartung, G.

G. Hartung, J. Hult, C. F. Kaminksi, J. W. Rogerson, and N. Swaminathan, “Effect of heat release on turbulence and scalar-turbulence interaction in premixed combustion,” Phys. Fluids 20, 035110 (2008).
[CrossRef]

Hassel, E. P.

E. P. Hassel and S. Linow, “Laser diagnostics for studies of turbulent combustion,” Meas. Sci. Technol. 11, R37-R57 (2000).
[CrossRef]

Heath, M.

M. Heath, S. Sarkar, T. Sanocki, and K. Bowyer, “Comparison of edge detectors: amethodology and initial study,” Comput. Vision Image Understand. 69, 38-54 (1998).
[CrossRef]

Hochgreb, S.

R. S. Barlow, G.-H. Wang, P. Anselmo-Filho, M. S.Sweeney, and S. Hochgreb, “Application of Raman/Rayleigh/LIF diagnostics in turbulent stratified flames,” Proc. Combust. Inst. 32, 945-953 (2008).
[CrossRef]

Hoover, A.

A. Hoover, G. Jean-Baptiste, X. Jiang, P. J. Flynn, H. Bunke, D. Goldof, and K. Bowyer, “Comparison of range image segmentation algorithms,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 673-689 (1996).
[CrossRef]

Hult, J.

G. Hartung, J. Hult, C. F. Kaminksi, J. W. Rogerson, and N. Swaminathan, “Effect of heat release on turbulence and scalar-turbulence interaction in premixed combustion,” Phys. Fluids 20, 035110 (2008).
[CrossRef]

S. Gashi, J. Hult, K. W. Jenkins, N. Chakraborty, S. Cant, and C. F. Kaminski, “Curvature and wrinkling of premixed flame kernels--comparisons of OH PLIF and DNS data,” Proc. Combust. Inst. 30, 809-817 (2005).
[CrossRef]

H. Malm, G. Sparr, J. Hult, and C. F. Kaminski, “Nonlinear diffusion filtering of images obtained by planar-laser-induced fluorescence spectroscopy,” J. Opt. Soc. Am. A 17, 2148-2156 (2000).
[CrossRef]

Jean-Baptiste, G.

A. Hoover, G. Jean-Baptiste, X. Jiang, P. J. Flynn, H. Bunke, D. Goldof, and K. Bowyer, “Comparison of range image segmentation algorithms,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 673-689 (1996).
[CrossRef]

Jenkins, K. W.

S. Gashi, J. Hult, K. W. Jenkins, N. Chakraborty, S. Cant, and C. F. Kaminski, “Curvature and wrinkling of premixed flame kernels--comparisons of OH PLIF and DNS data,” Proc. Combust. Inst. 30, 809-817 (2005).
[CrossRef]

Jiang, X.

A. Hoover, G. Jean-Baptiste, X. Jiang, P. J. Flynn, H. Bunke, D. Goldof, and K. Bowyer, “Comparison of range image segmentation algorithms,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 673-689 (1996).
[CrossRef]

Kaminksi, C. F.

G. Hartung, J. Hult, C. F. Kaminksi, J. W. Rogerson, and N. Swaminathan, “Effect of heat release on turbulence and scalar-turbulence interaction in premixed combustion,” Phys. Fluids 20, 035110 (2008).
[CrossRef]

Kaminski, C. F.

B. O. Ayoola, R. Balachandran, J. H. Frank, E. Mastorakos, and C. F. Kaminski, “Spatially resolved heat release rate measurements in turbulent premixed flames,” Combust. Flame 144, 1-16 (2006).
[CrossRef]

S. Gashi, J. Hult, K. W. Jenkins, N. Chakraborty, S. Cant, and C. F. Kaminski, “Curvature and wrinkling of premixed flame kernels--comparisons of OH PLIF and DNS data,” Proc. Combust. Inst. 30, 809-817 (2005).
[CrossRef]

H. Malm, G. Sparr, J. Hult, and C. F. Kaminski, “Nonlinear diffusion filtering of images obtained by planar-laser-induced fluorescence spectroscopy,” J. Opt. Soc. Am. A 17, 2148-2156 (2000).
[CrossRef]

Kitchen, L. J.

S. Venkatesh and L. J. Kitchen, “Edge evaluation using necessary components,” CVGIP: Graph. Models Image Process. 54, 23-30 (1992).
[CrossRef]

Kraemer, H.

H. Kraemer, Evaluating Medical Tests: Objective and Quantitative Guidelines (Sage Publications, 1992).

Linow, S.

E. P. Hassel and S. Linow, “Laser diagnostics for studies of turbulent combustion,” Meas. Sci. Technol. 11, R37-R57 (2000).
[CrossRef]

Luque-Escamilla, P. L.

R. Román, J. F. Gómez-Lopera, C. Atae-Allah, J. Martínez-Aroza, and P. L. Luque-Escamilla, “A measure of quality for evaluating methods of segmentation and edge detection,” Pattern Recogn. 34, 969-980 (2001).
[CrossRef]

Malah, D.

T. Peli and D. Malah, “A study of edge detection algorithms,” Comput. Graph. Image Process. 20, 1-21 (1982).
[CrossRef]

Malm, H.

Martel, C.

D. Veynante, J. Piana, J. M. Duclos, and C. Martel, “Experimental analysis of flame surface density models for premixed turbulent combustion,” Proc. Combust. Inst. 26, 413-420 (1996).

Martínez-Aroza, J.

R. Román, J. F. Gómez-Lopera, C. Atae-Allah, J. Martínez-Aroza, and P. L. Luque-Escamilla, “A measure of quality for evaluating methods of segmentation and edge detection,” Pattern Recogn. 34, 969-980 (2001).
[CrossRef]

Mastorakos, E.

B. O. Ayoola, R. Balachandran, J. H. Frank, E. Mastorakos, and C. F. Kaminski, “Spatially resolved heat release rate measurements in turbulent premixed flames,” Combust. Flame 144, 1-16 (2006).
[CrossRef]

Nobuyuki, O.

O. Nobuyuki, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man Cybern. 9, 62-66 (1979).
[CrossRef]

Peli, E.

Y. Yitzhaky and E. Peli, “A method for objectve edge detection evaluation and detector parameter selection,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1027-1033 (2003).
[CrossRef]

Peli, T.

T. Peli and D. Malah, “A study of edge detection algorithms,” Comput. Graph. Image Process. 20, 1-21 (1982).
[CrossRef]

Piana, J.

D. Veynante, J. Piana, J. M. Duclos, and C. Martel, “Experimental analysis of flame surface density models for premixed turbulent combustion,” Proc. Combust. Inst. 26, 413-420 (1996).

Prewitt, J. M. S.

J. M. S. Prewitt, “Object enhancement and extraction,” in Picture Processing and Psychopictorics, B. S. Lipkin and A. Rosenfeld, eds. (Academic, 1970), pp. 75-149

Raghupathy, K.

K. Raghupathy, “Curve tracing and curve detection in images,” M.S. thesis (Cornell University, 2004).

Roberts, L. G.

L. G. Roberts, “Machine perception of three dimensional solids,” in Optical and Electro-Optical Information Processing, J. Tippett, ed. (MIT Press, 1965), pp. 159-197

Rogerson, J. W.

G. Hartung, J. Hult, C. F. Kaminksi, J. W. Rogerson, and N. Swaminathan, “Effect of heat release on turbulence and scalar-turbulence interaction in premixed combustion,” Phys. Fluids 20, 035110 (2008).
[CrossRef]

Román, R.

R. Román, J. F. Gómez-Lopera, C. Atae-Allah, J. Martínez-Aroza, and P. L. Luque-Escamilla, “A measure of quality for evaluating methods of segmentation and edge detection,” Pattern Recogn. 34, 969-980 (2001).
[CrossRef]

Sanocki, T.

M. Heath, S. Sarkar, T. Sanocki, and K. Bowyer, “Comparison of edge detectors: amethodology and initial study,” Comput. Vision Image Understand. 69, 38-54 (1998).
[CrossRef]

Sarkar, S.

M. Heath, S. Sarkar, T. Sanocki, and K. Bowyer, “Comparison of edge detectors: amethodology and initial study,” Comput. Vision Image Understand. 69, 38-54 (1998).
[CrossRef]

Sobel, I.

I. Sobel, “Camera models and machine perception,” Ph.D. dissertation (Stanford University, 1970).

Sparr, G.

Swaminathan, N.

G. Hartung, J. Hult, C. F. Kaminksi, J. W. Rogerson, and N. Swaminathan, “Effect of heat release on turbulence and scalar-turbulence interaction in premixed combustion,” Phys. Fluids 20, 035110 (2008).
[CrossRef]

Sweeney, M. S.

R. S. Barlow, G.-H. Wang, P. Anselmo-Filho, M. S.Sweeney, and S. Hochgreb, “Application of Raman/Rayleigh/LIF diagnostics in turbulent stratified flames,” Proc. Combust. Inst. 32, 945-953 (2008).
[CrossRef]

Venkatesh, S.

S. Venkatesh and L. J. Kitchen, “Edge evaluation using necessary components,” CVGIP: Graph. Models Image Process. 54, 23-30 (1992).
[CrossRef]

Veynante, D.

D. Veynante, J. Piana, J. M. Duclos, and C. Martel, “Experimental analysis of flame surface density models for premixed turbulent combustion,” Proc. Combust. Inst. 26, 413-420 (1996).

Wang, G.-H.

R. S. Barlow, G.-H. Wang, P. Anselmo-Filho, M. S.Sweeney, and S. Hochgreb, “Application of Raman/Rayleigh/LIF diagnostics in turbulent stratified flames,” Proc. Combust. Inst. 32, 945-953 (2008).
[CrossRef]

Yitzhaky, Y.

Y. Yitzhaky and E. Peli, “A method for objectve edge detection evaluation and detector parameter selection,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1027-1033 (2003).
[CrossRef]

Zhang, Y. J.

Y. J. Zhang, “A survey on evaluation methods for image segmentation,” Pattern Recogn. 29, 1335-1346 (1996).
[CrossRef]

Combust. Flame

B. O. Ayoola, R. Balachandran, J. H. Frank, E. Mastorakos, and C. F. Kaminski, “Spatially resolved heat release rate measurements in turbulent premixed flames,” Combust. Flame 144, 1-16 (2006).
[CrossRef]

Comput. Graph. Image Process.

T. Peli and D. Malah, “A study of edge detection algorithms,” Comput. Graph. Image Process. 20, 1-21 (1982).
[CrossRef]

Comput. Vision Image Understand.

M. Heath, S. Sarkar, T. Sanocki, and K. Bowyer, “Comparison of edge detectors: amethodology and initial study,” Comput. Vision Image Understand. 69, 38-54 (1998).
[CrossRef]

CVGIP: Graph. Models Image Process.

S. Venkatesh and L. J. Kitchen, “Edge evaluation using necessary components,” CVGIP: Graph. Models Image Process. 54, 23-30 (1992).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell.

A. Hoover, G. Jean-Baptiste, X. Jiang, P. J. Flynn, H. Bunke, D. Goldof, and K. Bowyer, “Comparison of range image segmentation algorithms,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 673-689 (1996).
[CrossRef]

J. F. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8, 679-698(1986).
[CrossRef]

Y. Yitzhaky and E. Peli, “A method for objectve edge detection evaluation and detector parameter selection,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1027-1033 (2003).
[CrossRef]

IEEE Trans. Syst. Man Cybern.

O. Nobuyuki, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man Cybern. 9, 62-66 (1979).
[CrossRef]

J. Opt. Soc. Am. A

Meas. Sci. Technol.

E. P. Hassel and S. Linow, “Laser diagnostics for studies of turbulent combustion,” Meas. Sci. Technol. 11, R37-R57 (2000).
[CrossRef]

Pattern Recogn.

Y. J. Zhang, “A survey on evaluation methods for image segmentation,” Pattern Recogn. 29, 1335-1346 (1996).
[CrossRef]

R. Román, J. F. Gómez-Lopera, C. Atae-Allah, J. Martínez-Aroza, and P. L. Luque-Escamilla, “A measure of quality for evaluating methods of segmentation and edge detection,” Pattern Recogn. 34, 969-980 (2001).
[CrossRef]

Phys. Fluids

G. Hartung, J. Hult, C. F. Kaminksi, J. W. Rogerson, and N. Swaminathan, “Effect of heat release on turbulence and scalar-turbulence interaction in premixed combustion,” Phys. Fluids 20, 035110 (2008).
[CrossRef]

Proc. Combust. Inst.

R. S. Barlow, G.-H. Wang, P. Anselmo-Filho, M. S.Sweeney, and S. Hochgreb, “Application of Raman/Rayleigh/LIF diagnostics in turbulent stratified flames,” Proc. Combust. Inst. 32, 945-953 (2008).
[CrossRef]

D. Veynante, J. Piana, J. M. Duclos, and C. Martel, “Experimental analysis of flame surface density models for premixed turbulent combustion,” Proc. Combust. Inst. 26, 413-420 (1996).

S. Gashi, J. Hult, K. W. Jenkins, N. Chakraborty, S. Cant, and C. F. Kaminski, “Curvature and wrinkling of premixed flame kernels--comparisons of OH PLIF and DNS data,” Proc. Combust. Inst. 30, 809-817 (2005).
[CrossRef]

Other

L. G. Roberts, “Machine perception of three dimensional solids,” in Optical and Electro-Optical Information Processing, J. Tippett, ed. (MIT Press, 1965), pp. 159-197

J. M. S. Prewitt, “Object enhancement and extraction,” in Picture Processing and Psychopictorics, B. S. Lipkin and A. Rosenfeld, eds. (Academic, 1970), pp. 75-149

I. Sobel, “Camera models and machine perception,” Ph.D. dissertation (Stanford University, 1970).

K. Raghupathy, “Curve tracing and curve detection in images,” M.S. thesis (Cornell University, 2004).

H. Kraemer, Evaluating Medical Tests: Objective and Quantitative Guidelines (Sage Publications, 1992).

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Figures (15)

Fig. 1
Fig. 1

Profiles of OH signal and gradient across (a) an ideal premixed laminar flame calculation flame front and (b) an experimental premixed flame front. Distance x is nondimensionalized using the thermal flame thickness, δ = ( T T u ) / ( d T / d x max ) , where T is the equilibrium temperature and T u is the unburnt gas temperature, to give x ^ = x / δ . OH is normalized with respect to the maximum value recorded. The global equivalence ratio in both figures is ϕ g = 0.73 .

Fig. 2
Fig. 2

(a) Sample OH signal image, normalized between 0 (black) and 1 (white). (b) Histogram of nonlinear diffusion filtered image shaded in gray, with its Otsu threshold marked by a thick solid gray line . The histogram and corresponding Otsu threshold (- - - -) for the unfiltered image are shown in black, demonstrating the changes introduced by the nonlinear filtering. (c) Nonlinear-filtered Otsu-thresholded image showing unburnt reactants (i), burnt products (ii), and flame front (iii).

Fig. 3
Fig. 3

(a) Raw image normalized between 0 (black) and 1 (white), (b) Wiener-filtered image, (c) gradient map of filtered image, (d) unlinked flame front, (e) linked flame front with unlinked front shown in gray and linking segments in black, and (f) final flame front after flame pocket removal.

Fig. 4
Fig. 4

(a) Selection of experimental OH-PLIF images, where the OH signal is normalized between 0 (black) and 1 (white); (b) corresponding human-derived ground truths, where black pixels represent the flame front.

Fig. 5
Fig. 5

Three-dimensional surface of the gradient map of a Wiener filtered sample image at an intermediate stage of human derived ground truth extraction. The solid black line shows the edge midextraction, with the most recently selected edge point highlighted with an open black circle ().

Fig. 6
Fig. 6

Empirical investigation of the influence of σ max on (a) data rejection rates based on 1000 experimental images and (b) curvature extrema using RC2M. In (b) curvature extrema κ min and κ max are averaged over 25 sample experimental images for increasing σ search spaces to give κ min ¯ and κ max ¯ , which are plotted as a percentage of the corresponding HDGT value κ 0 .

Fig. 7
Fig. 7

Pixelwise comparison of various flame front extraction methods against HDGTs in terms of (a) curvature difference and (b) localization error where the limit of precision (2 pixels) is marked with a heavy black dashed line.

Fig. 8
Fig. 8

Curvature pdfs obtained using various flame front extraction methods. Each pdf contains over 50000 points divided into 50 equally spaced bins.

Fig. 9
Fig. 9

Reference flame front extracted by hand is compared against those extracted using a FC2M and NLOM. The alternate methods are only plotted for points deviating from the reference. Note the thresholding method results in a substantial section with localization errors greater than the hand precision level (2 pixels).

Fig. 10
Fig. 10

Pixelwise comparison of various flame front extraction methods against 1000 SDGTs (FC2M) in terms of (a) curvature difference and (b) localization error, where the limit of precision (0 pixels) is marked with a heavy black dashed line.

Fig. 11
Fig. 11

Frequency analysis of the optimal parameters chosen using (a) FC2M and (b) R2C2M on a set of 1000 experimental OH-PLIF images.

Fig. 12
Fig. 12

Investigation into the robustness of flame front identification using R2C2M (dotted line is the mean) and a priori parameter selection (dashed line is the mean). Note the rejection rate is plotted on the vertical axis using a log 10 scale.

Fig. 13
Fig. 13

Sample images used to investigate noise sensitivity: (a) raw experimental image, (b) image noised to give SNR of 10, and (c) image noised to give SNR of 2.

Fig. 14
Fig. 14

(a) Mean positive and negative curvature differences and (b) percentage of points located within the 0 pixel precision level (left y axis) and mean localization error (right y axis) for various SNR levels. R2C2M results are shown by black lines and NLOM by gray lines.

Fig. 15
Fig. 15

Percentage of images rejected using R2C2M and NLOM for various SNR levels. R2C2M results are shown by solid black lines and NLOM by solid gray lines.

Tables (5)

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Table 1 Receiver Operator Characteristics Definitions

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Table 2 Initial Parameter Sets for the Canny Algorithm Outlined in Subsection 2C

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Table 3 Reduced Parameter Search Space for RC2M

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Table 4 σ Ranges Using Recursive Reduced χ 2 Method (R2C2M)

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Table 5 Execution Timings (in Seconds) for Extraction Methods Implemented in MATLAB

Equations (5)

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SNR = μ signal μ background σ signal .
χ 2 = TPR TP FP 1 TP FP · TP + FP - FPR TP + FP .
CM = i = 1 N D i .
PGT i ( x , y ) = 1 if CM ( x , y ) i .
m × n × o × m + n + o + .

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