Abstract

With our single-wavelength spectral-imaging-based Thai jasmine rice identification system, we emphasize here that a combination of an appropriate polynomial fitting function on the determined chain code and a well-trained neural network configuration is highly sufficient in achieving a low false acceptance rate (FAR) and a low false rejection rate (FRR). Experimental demonstration shows promising results in identifying our desired Thai jasmine rice from six unwanted rice varieties with FAR and FRR values of 6.2% and 7.1%, respectively. Additional key performances include a much faster identification time of 30.5 s, chemical-free analysis, robustness, and adaptive learning.

© 2014 Optical Society of America

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References

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  1. K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Combination of simple chemical and spectroscopic methods for the identification of Thai Hom Mali rice,” Proc. SPIE 7315, 73150W (2009).
    [CrossRef]
  2. S. Sumriddetchkajorn, K. Suwansukho, and P. Buranasiri, “Identification of Thai Hom Mali rice using a refractometer,” Proc. SPIE 7315, 73150F (2009).
    [CrossRef]
  3. S. Sumriddetchkajorn and Y. Intaravanne, “Hyperspectral imaging-based credit card verifier structure with adaptive learning,” Appl. Opt. 47, 6594–6600 (2008).
    [CrossRef]
  4. S. Sumriddetchkajorn and Y. Intaravanne, “Data-nonintrusive photonics-based credit card verifier with a low false rejection rate,” Appl. Opt. 49, 764–770 (2010).
    [CrossRef]
  5. S. Sumriddetchkajorn and Y. Intaravanne, “Evolution of optically nondestructive and data-non-intrusive credit card verifiers,” Proc. SPIE 7726, 77261B (2010).
    [CrossRef]
  6. Y. Intaravanne, S. Sumriddetchkajorn, and J. Nukeaw, “Cell phone-based two-dimensional spectral analysis for banana ripeness estimation,” Sens. Actuators B 168, 390–394 (2012).
    [CrossRef]
  7. S. Sumriddetchkajorn and C. Kamtongdee, “Optical penetration-based silkworm pupa gender sensor structure,” Appl. Opt. 51, 408–412 (2012).
    [CrossRef]
  8. C. Kamtongdee, S. Sumriddetchkajorn, and C. Sa-ngiamsak, “Feasibility study of silkworm pupa sex identification with pattern matching,” Comput. Eletron. Agric. 95, 31–37 (2013).
    [CrossRef]
  9. S. Sumriddetchkajorn, C. Kamtongdee, and C. Sa-ngiamsak, “Spectral imaging analysis for silkworm gender classification,” Proc. SPIE 8881, 888106 (2013).
    [CrossRef]
  10. S. Janchaysang, S. Sumriddetchkajorn, and P. Buranasiri, “Tunable filter-based multispectral imaging for detection of blood stains on construction material substrates. Part 1. Developing blood stain discrimination criteria,” Appl. Opt. 51, 6984–6996 (2012).
    [CrossRef]
  11. S. Janchaysang, S. Sumriddetchkajorn, and P. Buranasiri, “Improved tunable filter-based multispectral imaging system for detection of blood stains on construction material substrates,” Proc. SPIE 8883, 888316 (2013).
    [CrossRef]
  12. S. Janchaysang, S. Sumriddetchkajorn, and P. Buranasiri, “Tunable filter-based multispectral imaging for detection of blood stains on construction material substrates. Part 2,” Appl. Opt. 52, 4898–4910 (2013).
    [CrossRef]
  13. S. Sumriddetchkajorn, K. Suwansukho, and P. Buranasiri, “Two-wavelength spectral imaging-based Thai rice breed identification,” Proc. SPIE 7715, 77150I (2010).
    [CrossRef]
  14. K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Demonstration of a single wavelength spectral-imaging-based Thai jasmine rice identification,” Appl. Opt. 50, 4024–4030 (2011).
    [CrossRef]
  15. K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Improvement of single-wavelength based Thai jasmine rice identification with elliptic Fourier descriptor and neural network analysis,” Proc. SPIE 8558, 85580C (2012).
    [CrossRef]
  16. I. Sadeh, “Polynomial approximation of image,” Comput. Math. Appl. 32, 99–115 (1996).
    [CrossRef]
  17. L. M. Kocic and G. V. Milovanovic, “Shape preserving approximations by polynomial and splines,” Comput. Math. Appl. 33, 59–97 (1997).
    [CrossRef]
  18. P. M. Baggenstoss, “Image distortion analysis using polynomial series expansion,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1438–1451 (2004).
    [CrossRef]
  19. A. Kaveti, K. K. Teoh, and H. Wang, “Second order implicit polynomials for segmentation of range images,” Pattern Recogn. 29, 937–949 (1996).
    [CrossRef]
  20. K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Single-wavelength based Thai jasmine rice identification with polynomial fitting function and neural network analysis,” Proc. SPIE 8883, 888318 (2013).
    [CrossRef]
  21. H. Freeman, “On the encoding of arbitrary geometric configuration,” IRE Trans Electron Comput EC-10, 260–268 (1961).
    [CrossRef]
  22. W. Burger and M. J. Burge, Digital Image Processing: An Algorithmic Introduction Using Java (Springer2008).
  23. Y. Mingqiang, K. Kidiyo, and R. Joseph, “A survey of shape feature extraction techniques,” 2008, http://www.intechopen.com/books/pattern_recognition_techniques_technology_and_applications/a_survey_of_shape_feature_extraction_techniques .
  24. J. L. Harper, P. H. Lovell, and K. G. Moore, “The shapes and sizes of seeds,” Annu. Rev. Ecol. Syst. 1, 327–356 (1970).
    [CrossRef]

2013

C. Kamtongdee, S. Sumriddetchkajorn, and C. Sa-ngiamsak, “Feasibility study of silkworm pupa sex identification with pattern matching,” Comput. Eletron. Agric. 95, 31–37 (2013).
[CrossRef]

S. Sumriddetchkajorn, C. Kamtongdee, and C. Sa-ngiamsak, “Spectral imaging analysis for silkworm gender classification,” Proc. SPIE 8881, 888106 (2013).
[CrossRef]

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Single-wavelength based Thai jasmine rice identification with polynomial fitting function and neural network analysis,” Proc. SPIE 8883, 888318 (2013).
[CrossRef]

S. Janchaysang, S. Sumriddetchkajorn, and P. Buranasiri, “Improved tunable filter-based multispectral imaging system for detection of blood stains on construction material substrates,” Proc. SPIE 8883, 888316 (2013).
[CrossRef]

S. Janchaysang, S. Sumriddetchkajorn, and P. Buranasiri, “Tunable filter-based multispectral imaging for detection of blood stains on construction material substrates. Part 2,” Appl. Opt. 52, 4898–4910 (2013).
[CrossRef]

2012

S. Sumriddetchkajorn and C. Kamtongdee, “Optical penetration-based silkworm pupa gender sensor structure,” Appl. Opt. 51, 408–412 (2012).
[CrossRef]

S. Janchaysang, S. Sumriddetchkajorn, and P. Buranasiri, “Tunable filter-based multispectral imaging for detection of blood stains on construction material substrates. Part 1. Developing blood stain discrimination criteria,” Appl. Opt. 51, 6984–6996 (2012).
[CrossRef]

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Improvement of single-wavelength based Thai jasmine rice identification with elliptic Fourier descriptor and neural network analysis,” Proc. SPIE 8558, 85580C (2012).
[CrossRef]

Y. Intaravanne, S. Sumriddetchkajorn, and J. Nukeaw, “Cell phone-based two-dimensional spectral analysis for banana ripeness estimation,” Sens. Actuators B 168, 390–394 (2012).
[CrossRef]

2011

2010

S. Sumriddetchkajorn and Y. Intaravanne, “Data-nonintrusive photonics-based credit card verifier with a low false rejection rate,” Appl. Opt. 49, 764–770 (2010).
[CrossRef]

S. Sumriddetchkajorn and Y. Intaravanne, “Evolution of optically nondestructive and data-non-intrusive credit card verifiers,” Proc. SPIE 7726, 77261B (2010).
[CrossRef]

S. Sumriddetchkajorn, K. Suwansukho, and P. Buranasiri, “Two-wavelength spectral imaging-based Thai rice breed identification,” Proc. SPIE 7715, 77150I (2010).
[CrossRef]

2009

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Combination of simple chemical and spectroscopic methods for the identification of Thai Hom Mali rice,” Proc. SPIE 7315, 73150W (2009).
[CrossRef]

S. Sumriddetchkajorn, K. Suwansukho, and P. Buranasiri, “Identification of Thai Hom Mali rice using a refractometer,” Proc. SPIE 7315, 73150F (2009).
[CrossRef]

2008

2004

P. M. Baggenstoss, “Image distortion analysis using polynomial series expansion,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1438–1451 (2004).
[CrossRef]

1997

L. M. Kocic and G. V. Milovanovic, “Shape preserving approximations by polynomial and splines,” Comput. Math. Appl. 33, 59–97 (1997).
[CrossRef]

1996

A. Kaveti, K. K. Teoh, and H. Wang, “Second order implicit polynomials for segmentation of range images,” Pattern Recogn. 29, 937–949 (1996).
[CrossRef]

I. Sadeh, “Polynomial approximation of image,” Comput. Math. Appl. 32, 99–115 (1996).
[CrossRef]

1970

J. L. Harper, P. H. Lovell, and K. G. Moore, “The shapes and sizes of seeds,” Annu. Rev. Ecol. Syst. 1, 327–356 (1970).
[CrossRef]

1961

H. Freeman, “On the encoding of arbitrary geometric configuration,” IRE Trans Electron Comput EC-10, 260–268 (1961).
[CrossRef]

Baggenstoss, P. M.

P. M. Baggenstoss, “Image distortion analysis using polynomial series expansion,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1438–1451 (2004).
[CrossRef]

Buranasiri, P.

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Single-wavelength based Thai jasmine rice identification with polynomial fitting function and neural network analysis,” Proc. SPIE 8883, 888318 (2013).
[CrossRef]

S. Janchaysang, S. Sumriddetchkajorn, and P. Buranasiri, “Tunable filter-based multispectral imaging for detection of blood stains on construction material substrates. Part 2,” Appl. Opt. 52, 4898–4910 (2013).
[CrossRef]

S. Janchaysang, S. Sumriddetchkajorn, and P. Buranasiri, “Improved tunable filter-based multispectral imaging system for detection of blood stains on construction material substrates,” Proc. SPIE 8883, 888316 (2013).
[CrossRef]

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Improvement of single-wavelength based Thai jasmine rice identification with elliptic Fourier descriptor and neural network analysis,” Proc. SPIE 8558, 85580C (2012).
[CrossRef]

S. Janchaysang, S. Sumriddetchkajorn, and P. Buranasiri, “Tunable filter-based multispectral imaging for detection of blood stains on construction material substrates. Part 1. Developing blood stain discrimination criteria,” Appl. Opt. 51, 6984–6996 (2012).
[CrossRef]

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Demonstration of a single wavelength spectral-imaging-based Thai jasmine rice identification,” Appl. Opt. 50, 4024–4030 (2011).
[CrossRef]

S. Sumriddetchkajorn, K. Suwansukho, and P. Buranasiri, “Two-wavelength spectral imaging-based Thai rice breed identification,” Proc. SPIE 7715, 77150I (2010).
[CrossRef]

S. Sumriddetchkajorn, K. Suwansukho, and P. Buranasiri, “Identification of Thai Hom Mali rice using a refractometer,” Proc. SPIE 7315, 73150F (2009).
[CrossRef]

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Combination of simple chemical and spectroscopic methods for the identification of Thai Hom Mali rice,” Proc. SPIE 7315, 73150W (2009).
[CrossRef]

Burge, M. J.

W. Burger and M. J. Burge, Digital Image Processing: An Algorithmic Introduction Using Java (Springer2008).

Burger, W.

W. Burger and M. J. Burge, Digital Image Processing: An Algorithmic Introduction Using Java (Springer2008).

Freeman, H.

H. Freeman, “On the encoding of arbitrary geometric configuration,” IRE Trans Electron Comput EC-10, 260–268 (1961).
[CrossRef]

Harper, J. L.

J. L. Harper, P. H. Lovell, and K. G. Moore, “The shapes and sizes of seeds,” Annu. Rev. Ecol. Syst. 1, 327–356 (1970).
[CrossRef]

Intaravanne, Y.

Y. Intaravanne, S. Sumriddetchkajorn, and J. Nukeaw, “Cell phone-based two-dimensional spectral analysis for banana ripeness estimation,” Sens. Actuators B 168, 390–394 (2012).
[CrossRef]

S. Sumriddetchkajorn and Y. Intaravanne, “Evolution of optically nondestructive and data-non-intrusive credit card verifiers,” Proc. SPIE 7726, 77261B (2010).
[CrossRef]

S. Sumriddetchkajorn and Y. Intaravanne, “Data-nonintrusive photonics-based credit card verifier with a low false rejection rate,” Appl. Opt. 49, 764–770 (2010).
[CrossRef]

S. Sumriddetchkajorn and Y. Intaravanne, “Hyperspectral imaging-based credit card verifier structure with adaptive learning,” Appl. Opt. 47, 6594–6600 (2008).
[CrossRef]

Janchaysang, S.

Kamtongdee, C.

S. Sumriddetchkajorn, C. Kamtongdee, and C. Sa-ngiamsak, “Spectral imaging analysis for silkworm gender classification,” Proc. SPIE 8881, 888106 (2013).
[CrossRef]

C. Kamtongdee, S. Sumriddetchkajorn, and C. Sa-ngiamsak, “Feasibility study of silkworm pupa sex identification with pattern matching,” Comput. Eletron. Agric. 95, 31–37 (2013).
[CrossRef]

S. Sumriddetchkajorn and C. Kamtongdee, “Optical penetration-based silkworm pupa gender sensor structure,” Appl. Opt. 51, 408–412 (2012).
[CrossRef]

Kaveti, A.

A. Kaveti, K. K. Teoh, and H. Wang, “Second order implicit polynomials for segmentation of range images,” Pattern Recogn. 29, 937–949 (1996).
[CrossRef]

Kocic, L. M.

L. M. Kocic and G. V. Milovanovic, “Shape preserving approximations by polynomial and splines,” Comput. Math. Appl. 33, 59–97 (1997).
[CrossRef]

Lovell, P. H.

J. L. Harper, P. H. Lovell, and K. G. Moore, “The shapes and sizes of seeds,” Annu. Rev. Ecol. Syst. 1, 327–356 (1970).
[CrossRef]

Milovanovic, G. V.

L. M. Kocic and G. V. Milovanovic, “Shape preserving approximations by polynomial and splines,” Comput. Math. Appl. 33, 59–97 (1997).
[CrossRef]

Moore, K. G.

J. L. Harper, P. H. Lovell, and K. G. Moore, “The shapes and sizes of seeds,” Annu. Rev. Ecol. Syst. 1, 327–356 (1970).
[CrossRef]

Nukeaw, J.

Y. Intaravanne, S. Sumriddetchkajorn, and J. Nukeaw, “Cell phone-based two-dimensional spectral analysis for banana ripeness estimation,” Sens. Actuators B 168, 390–394 (2012).
[CrossRef]

Sadeh, I.

I. Sadeh, “Polynomial approximation of image,” Comput. Math. Appl. 32, 99–115 (1996).
[CrossRef]

Sa-ngiamsak, C.

C. Kamtongdee, S. Sumriddetchkajorn, and C. Sa-ngiamsak, “Feasibility study of silkworm pupa sex identification with pattern matching,” Comput. Eletron. Agric. 95, 31–37 (2013).
[CrossRef]

S. Sumriddetchkajorn, C. Kamtongdee, and C. Sa-ngiamsak, “Spectral imaging analysis for silkworm gender classification,” Proc. SPIE 8881, 888106 (2013).
[CrossRef]

Sumriddetchkajorn, S.

S. Sumriddetchkajorn, C. Kamtongdee, and C. Sa-ngiamsak, “Spectral imaging analysis for silkworm gender classification,” Proc. SPIE 8881, 888106 (2013).
[CrossRef]

C. Kamtongdee, S. Sumriddetchkajorn, and C. Sa-ngiamsak, “Feasibility study of silkworm pupa sex identification with pattern matching,” Comput. Eletron. Agric. 95, 31–37 (2013).
[CrossRef]

S. Janchaysang, S. Sumriddetchkajorn, and P. Buranasiri, “Tunable filter-based multispectral imaging for detection of blood stains on construction material substrates. Part 2,” Appl. Opt. 52, 4898–4910 (2013).
[CrossRef]

S. Janchaysang, S. Sumriddetchkajorn, and P. Buranasiri, “Improved tunable filter-based multispectral imaging system for detection of blood stains on construction material substrates,” Proc. SPIE 8883, 888316 (2013).
[CrossRef]

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Single-wavelength based Thai jasmine rice identification with polynomial fitting function and neural network analysis,” Proc. SPIE 8883, 888318 (2013).
[CrossRef]

S. Sumriddetchkajorn and C. Kamtongdee, “Optical penetration-based silkworm pupa gender sensor structure,” Appl. Opt. 51, 408–412 (2012).
[CrossRef]

Y. Intaravanne, S. Sumriddetchkajorn, and J. Nukeaw, “Cell phone-based two-dimensional spectral analysis for banana ripeness estimation,” Sens. Actuators B 168, 390–394 (2012).
[CrossRef]

S. Janchaysang, S. Sumriddetchkajorn, and P. Buranasiri, “Tunable filter-based multispectral imaging for detection of blood stains on construction material substrates. Part 1. Developing blood stain discrimination criteria,” Appl. Opt. 51, 6984–6996 (2012).
[CrossRef]

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Improvement of single-wavelength based Thai jasmine rice identification with elliptic Fourier descriptor and neural network analysis,” Proc. SPIE 8558, 85580C (2012).
[CrossRef]

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Demonstration of a single wavelength spectral-imaging-based Thai jasmine rice identification,” Appl. Opt. 50, 4024–4030 (2011).
[CrossRef]

S. Sumriddetchkajorn, K. Suwansukho, and P. Buranasiri, “Two-wavelength spectral imaging-based Thai rice breed identification,” Proc. SPIE 7715, 77150I (2010).
[CrossRef]

S. Sumriddetchkajorn and Y. Intaravanne, “Data-nonintrusive photonics-based credit card verifier with a low false rejection rate,” Appl. Opt. 49, 764–770 (2010).
[CrossRef]

S. Sumriddetchkajorn and Y. Intaravanne, “Evolution of optically nondestructive and data-non-intrusive credit card verifiers,” Proc. SPIE 7726, 77261B (2010).
[CrossRef]

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Combination of simple chemical and spectroscopic methods for the identification of Thai Hom Mali rice,” Proc. SPIE 7315, 73150W (2009).
[CrossRef]

S. Sumriddetchkajorn, K. Suwansukho, and P. Buranasiri, “Identification of Thai Hom Mali rice using a refractometer,” Proc. SPIE 7315, 73150F (2009).
[CrossRef]

S. Sumriddetchkajorn and Y. Intaravanne, “Hyperspectral imaging-based credit card verifier structure with adaptive learning,” Appl. Opt. 47, 6594–6600 (2008).
[CrossRef]

Suwansukho, K.

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Single-wavelength based Thai jasmine rice identification with polynomial fitting function and neural network analysis,” Proc. SPIE 8883, 888318 (2013).
[CrossRef]

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Improvement of single-wavelength based Thai jasmine rice identification with elliptic Fourier descriptor and neural network analysis,” Proc. SPIE 8558, 85580C (2012).
[CrossRef]

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Demonstration of a single wavelength spectral-imaging-based Thai jasmine rice identification,” Appl. Opt. 50, 4024–4030 (2011).
[CrossRef]

S. Sumriddetchkajorn, K. Suwansukho, and P. Buranasiri, “Two-wavelength spectral imaging-based Thai rice breed identification,” Proc. SPIE 7715, 77150I (2010).
[CrossRef]

S. Sumriddetchkajorn, K. Suwansukho, and P. Buranasiri, “Identification of Thai Hom Mali rice using a refractometer,” Proc. SPIE 7315, 73150F (2009).
[CrossRef]

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Combination of simple chemical and spectroscopic methods for the identification of Thai Hom Mali rice,” Proc. SPIE 7315, 73150W (2009).
[CrossRef]

Teoh, K. K.

A. Kaveti, K. K. Teoh, and H. Wang, “Second order implicit polynomials for segmentation of range images,” Pattern Recogn. 29, 937–949 (1996).
[CrossRef]

Wang, H.

A. Kaveti, K. K. Teoh, and H. Wang, “Second order implicit polynomials for segmentation of range images,” Pattern Recogn. 29, 937–949 (1996).
[CrossRef]

Annu. Rev. Ecol. Syst.

J. L. Harper, P. H. Lovell, and K. G. Moore, “The shapes and sizes of seeds,” Annu. Rev. Ecol. Syst. 1, 327–356 (1970).
[CrossRef]

Appl. Opt.

Comput. Eletron. Agric.

C. Kamtongdee, S. Sumriddetchkajorn, and C. Sa-ngiamsak, “Feasibility study of silkworm pupa sex identification with pattern matching,” Comput. Eletron. Agric. 95, 31–37 (2013).
[CrossRef]

Comput. Math. Appl.

I. Sadeh, “Polynomial approximation of image,” Comput. Math. Appl. 32, 99–115 (1996).
[CrossRef]

L. M. Kocic and G. V. Milovanovic, “Shape preserving approximations by polynomial and splines,” Comput. Math. Appl. 33, 59–97 (1997).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell.

P. M. Baggenstoss, “Image distortion analysis using polynomial series expansion,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1438–1451 (2004).
[CrossRef]

IRE Trans Electron Comput

H. Freeman, “On the encoding of arbitrary geometric configuration,” IRE Trans Electron Comput EC-10, 260–268 (1961).
[CrossRef]

Pattern Recogn.

A. Kaveti, K. K. Teoh, and H. Wang, “Second order implicit polynomials for segmentation of range images,” Pattern Recogn. 29, 937–949 (1996).
[CrossRef]

Proc. SPIE

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Single-wavelength based Thai jasmine rice identification with polynomial fitting function and neural network analysis,” Proc. SPIE 8883, 888318 (2013).
[CrossRef]

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Combination of simple chemical and spectroscopic methods for the identification of Thai Hom Mali rice,” Proc. SPIE 7315, 73150W (2009).
[CrossRef]

S. Sumriddetchkajorn, K. Suwansukho, and P. Buranasiri, “Identification of Thai Hom Mali rice using a refractometer,” Proc. SPIE 7315, 73150F (2009).
[CrossRef]

S. Sumriddetchkajorn and Y. Intaravanne, “Evolution of optically nondestructive and data-non-intrusive credit card verifiers,” Proc. SPIE 7726, 77261B (2010).
[CrossRef]

S. Sumriddetchkajorn, C. Kamtongdee, and C. Sa-ngiamsak, “Spectral imaging analysis for silkworm gender classification,” Proc. SPIE 8881, 888106 (2013).
[CrossRef]

K. Suwansukho, S. Sumriddetchkajorn, and P. Buranasiri, “Improvement of single-wavelength based Thai jasmine rice identification with elliptic Fourier descriptor and neural network analysis,” Proc. SPIE 8558, 85580C (2012).
[CrossRef]

S. Janchaysang, S. Sumriddetchkajorn, and P. Buranasiri, “Improved tunable filter-based multispectral imaging system for detection of blood stains on construction material substrates,” Proc. SPIE 8883, 888316 (2013).
[CrossRef]

S. Sumriddetchkajorn, K. Suwansukho, and P. Buranasiri, “Two-wavelength spectral imaging-based Thai rice breed identification,” Proc. SPIE 7715, 77150I (2010).
[CrossRef]

Sens. Actuators B

Y. Intaravanne, S. Sumriddetchkajorn, and J. Nukeaw, “Cell phone-based two-dimensional spectral analysis for banana ripeness estimation,” Sens. Actuators B 168, 390–394 (2012).
[CrossRef]

Other

W. Burger and M. J. Burge, Digital Image Processing: An Algorithmic Introduction Using Java (Springer2008).

Y. Mingqiang, K. Kidiyo, and R. Joseph, “A survey of shape feature extraction techniques,” 2008, http://www.intechopen.com/books/pattern_recognition_techniques_technology_and_applications/a_survey_of_shape_feature_extraction_techniques .

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Figures (12)

Fig. 1.
Fig. 1.

Structure of our single-wavelength spectral imaging-based Thai jasmine rice identification system.

Fig. 2.
Fig. 2.

Our rice tray with 20 milled rice grains on the rice slot.

Fig. 3.
Fig. 3.

(a) Binary edge image of a shape pattern with the specific line connection of chain codes starting from upper-left-hand corner and (b) eight specific line directions.

Fig. 4.
Fig. 4.

Zoomed images of Thai milled rice grains from six varieties: (a) CNT1, (b) HPSL2, (c) HSPR60, (d) KDML105, (e) PTT1, (f) RD15, and (g) RD23.

Fig. 5.
Fig. 5.

(a) 235×305 pixel image size of a spectral image of milled rice grains and (b) its threshold image.

Fig. 6.
Fig. 6.

(a) Boundary of the threshold image computed from the chain coding process. (b) and (c) Line segments of the image boundary projected on the x and y axes, respectively.

Fig. 7.
Fig. 7.

Fitted line segments of the travelling distances along the (a) x axis and (b) y axis. (c) Reconstructed image contour from the fitted line segments in (a) and (b).

Fig. 8.
Fig. 8.

Plotted graphs of the extracted (a) perimeter, (b) area, and (c) eccentricity parameter.

Fig. 9.
Fig. 9.

Principal component analysis of three shape parameters.

Fig. 10.
Fig. 10.

FFNNA results during (a) the training period and (b) test with the unknown data set.

Fig. 11.
Fig. 11.

Examples of two spectral images used during field operation tests.

Fig. 12.
Fig. 12.

Testing results of KDML105 identification in practice.

Tables (1)

Tables Icon

Table 1. Average Values of Three Shape Parameters

Equations (12)

Equations on this page are rendered with MathJax. Learn more.

I(x,y)={255,ThminI(x,y)Thmax0,Otherwise.
Δti=1+(212)(1(1)ci),
Δxi=signum(6ci)signum(2ci),
Δyi=signum(4ci)signum(ci),
signum(z)={1z>00z=01z<0.
xp=i=1pΔxi,
yp=i=1pΔyi.
f(x)=anxn+an1xn1++a2x2+a1x+a0.
Perimeter=0.95·i=0p1length(ci),
Area=12|i=0p1(xiyi+1xi+1yi)|,
length(c)={1forc=0,2,4,6,2forc=1,3,5,7.
Eccentricity=λ2λ1.

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