Abstract

Automatic operations play an important role in societies by saving time and improving efficiency. In this paper, we apply the digital image processing method to the field of lumbering to automatically calculate tree diameters in order to reduce culler work and enable a third party to verify tree diameters. To calculate the cross-sectional diameter of a tree, the image was first segmented by the marker-controlled watershed transform algorithm based on the hue saturation intensity (HSI) color model. Then, the tree diameter was obtained by measuring the area of every isolated region in the segmented image. Finally, the true diameter was calculated by multiplying the diameter computed in the image and the scale, which was derived from the baseline and disparity of correspondence points from stereoscopic image pairs captured by rectified configuration cameras.

© 2012 Optical Society of America

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    [CrossRef]
  13. J. Chen and S. Liu, “A medical image segmentation method based on watershed transform,” The Fifth International Conference on Computer and Information Technology (IEEE, 2005), pp. 634–638.
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    [CrossRef]
  15. X. Guo and B. Guo, “Color image morphology based on distance in the HSI color space,” in ISECS International Colloquium on Computing, Communication, Control, and Management, Vol. 3 (IEEE, 2009), pp. 264–267.
  16. M. Osman, M. Mashor, H. Jaafar, R. Raof, and N. Harun, “Performance comparison between RGB and HSI linear stretching for tuberculosis bacilli detection in Ziehl-Neelsen tissue slide images,” in Proceedings of IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (IEEE, 2010), pp. 357–362.
  17. Z. Cheng and R. Chen, “License plate location method based on modified HSI model of color image,” in International Conference on Electronic Measurement & Instruments, ICEMI ’09 (IEEE, 2009), pp. 4197–4201.
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    [CrossRef]
  19. P. Soille, Morphological Image Analysis: Principles and Application (Springer-Verlag, 2003).
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  23. J. Qiu, Y. Lu, T. Huang, and T. Ikenaga, “An FPGA-based real-time hardware accelerator for orientation calculation part in SIFT,” in Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009, IIH-MSP ’09 (IEEE, 2009), pp. 1334–1337.
  24. K. Li and S. Zhou, “A fast SIFT feature matching algorithm for image registration,” in 2011 International Conference on Multimedia and Signal Processing (CMSP), Vol. 1 (IEEE, 2011), pp. 89–93.
  25. D. Lowe, “Demo software: SIFT Keypoint Detector,” http://www.cs.ubc.ca/~lowe/keypoints .

2010

I. Karoui, R. Fablet, J. Boucher, and J. Augustin, “Variational region-based segmentation using multiple texture statistics,” IEEE Trans. Image Process. 19, 3146–3156 (2010).

C. Jung and C. Kim, “Segmentation clustered nuclei using H-minima transform-based marker extraction and contour parameterization,” IEEE Trans. Biomed. Eng. 57, 2600–2604 (2010).

2007

2006

X. Yang, H. Li, and X. Zhou, “Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and Kalman filter in time-lapse microscopy,” IEEE Trans. Circuits Syst. 53, 2405–2414 (2006).
[CrossRef]

M. Danesh Panah and B. Javidi, “Segmentation of 3D holographic images using bivariate jointly distributed region snake,” Opt. Express 14, 5143–5153 (2006).
[CrossRef]

2004

V. Kolmogorov and R. Zabin, “What energy functions can be minimized via graph cuts?” IEEE Trans. Pattern Anal. Machine Intell. 26, 147–159 (2004).
[CrossRef]

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004).
[CrossRef]

2002

C. Riddell, P. Brigger, R. Carson, and S. Bacharach, “The watershed algorithm: a method to segment noisy PET transmission images,” IEEE Trans. Nucl. Sci. 46, 713–719 (2002).
[CrossRef]

1999

C. Chesnaud, P. Refregier, and V. Boulet, “Statistical region snake-based segmentation adapted to different physical noise models,” IEEE Trans. Pattern Anal. Machine Intell. 21, 1145–1157 (1999).

1998

1996

Augustin, J.

I. Karoui, R. Fablet, J. Boucher, and J. Augustin, “Variational region-based segmentation using multiple texture statistics,” IEEE Trans. Image Process. 19, 3146–3156 (2010).

Bacharach, S.

C. Riddell, P. Brigger, R. Carson, and S. Bacharach, “The watershed algorithm: a method to segment noisy PET transmission images,” IEEE Trans. Nucl. Sci. 46, 713–719 (2002).
[CrossRef]

Boucher, J.

I. Karoui, R. Fablet, J. Boucher, and J. Augustin, “Variational region-based segmentation using multiple texture statistics,” IEEE Trans. Image Process. 19, 3146–3156 (2010).

Boulet, V.

C. Chesnaud, P. Refregier, and V. Boulet, “Statistical region snake-based segmentation adapted to different physical noise models,” IEEE Trans. Pattern Anal. Machine Intell. 21, 1145–1157 (1999).

Boyle, R.

M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision (Thomson, 2008).

Brigger, P.

C. Riddell, P. Brigger, R. Carson, and S. Bacharach, “The watershed algorithm: a method to segment noisy PET transmission images,” IEEE Trans. Nucl. Sci. 46, 713–719 (2002).
[CrossRef]

Carson, R.

C. Riddell, P. Brigger, R. Carson, and S. Bacharach, “The watershed algorithm: a method to segment noisy PET transmission images,” IEEE Trans. Nucl. Sci. 46, 713–719 (2002).
[CrossRef]

Castro, A.

Chen, J.

J. Chen and S. Liu, “A medical image segmentation method based on watershed transform,” The Fifth International Conference on Computer and Information Technology (IEEE, 2005), pp. 634–638.

Chen, R.

Z. Cheng and R. Chen, “License plate location method based on modified HSI model of color image,” in International Conference on Electronic Measurement & Instruments, ICEMI ’09 (IEEE, 2009), pp. 4197–4201.

Cheng, Z.

Z. Cheng and R. Chen, “License plate location method based on modified HSI model of color image,” in International Conference on Electronic Measurement & Instruments, ICEMI ’09 (IEEE, 2009), pp. 4197–4201.

Chesnaud, C.

C. Chesnaud, P. Refregier, and V. Boulet, “Statistical region snake-based segmentation adapted to different physical noise models,” IEEE Trans. Pattern Anal. Machine Intell. 21, 1145–1157 (1999).

C. Chesnaud, V. Page, and P. Refregier, “Improvement in robustness of the statistically independent region snake-based segmentation method of target-shape tracking,” Opt. Lett. 23, 488–490 (1998).
[CrossRef]

Danesh Panah, M.

Fabijanska, A.

A. Fabijanska, “A survey of thresholding algorithms on yarn images,” in 2010 Proceedings of VIth Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH) (IEEE, 2010), pp. 23–28.

Fablet, R.

I. Karoui, R. Fablet, J. Boucher, and J. Augustin, “Variational region-based segmentation using multiple texture statistics,” IEEE Trans. Image Process. 19, 3146–3156 (2010).

Frauel, Y.

Germain, O.

Gonzalez, R. C.

R. C. Gonzalez and R. E. Woods, Digital Imaging Processing (Prentice-Hall, 2002).

Guo, B.

X. Guo and B. Guo, “Color image morphology based on distance in the HSI color space,” in ISECS International Colloquium on Computing, Communication, Control, and Management, Vol. 3 (IEEE, 2009), pp. 264–267.

Guo, X.

X. Guo and B. Guo, “Color image morphology based on distance in the HSI color space,” in ISECS International Colloquium on Computing, Communication, Control, and Management, Vol. 3 (IEEE, 2009), pp. 264–267.

Harun, N.

M. Osman, M. Mashor, H. Jaafar, R. Raof, and N. Harun, “Performance comparison between RGB and HSI linear stretching for tuberculosis bacilli detection in Ziehl-Neelsen tissue slide images,” in Proceedings of IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (IEEE, 2010), pp. 357–362.

Hlavac, V.

M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision (Thomson, 2008).

Huang, T.

J. Qiu, Y. Lu, T. Huang, and T. Ikenaga, “An FPGA-based real-time hardware accelerator for orientation calculation part in SIFT,” in Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009, IIH-MSP ’09 (IEEE, 2009), pp. 1334–1337.

Ikenaga, T.

J. Qiu, Y. Lu, T. Huang, and T. Ikenaga, “An FPGA-based real-time hardware accelerator for orientation calculation part in SIFT,” in Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009, IIH-MSP ’09 (IEEE, 2009), pp. 1334–1337.

Irshad, H.

C. Naz, H. Majeed, and H. Irshad, “Image segmentation using fuzzy clustering: a survey,” in Proceedings of IEEE Conference on ICET (IEEE, 2010), pp. 181–184.

Jaafar, H.

M. Osman, M. Mashor, H. Jaafar, R. Raof, and N. Harun, “Performance comparison between RGB and HSI linear stretching for tuberculosis bacilli detection in Ziehl-Neelsen tissue slide images,” in Proceedings of IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (IEEE, 2010), pp. 357–362.

Javidi, B.

Jung, C.

C. Jung and C. Kim, “Segmentation clustered nuclei using H-minima transform-based marker extraction and contour parameterization,” IEEE Trans. Biomed. Eng. 57, 2600–2604 (2010).

Karoui, I.

I. Karoui, R. Fablet, J. Boucher, and J. Augustin, “Variational region-based segmentation using multiple texture statistics,” IEEE Trans. Image Process. 19, 3146–3156 (2010).

Kim, C.

C. Jung and C. Kim, “Segmentation clustered nuclei using H-minima transform-based marker extraction and contour parameterization,” IEEE Trans. Biomed. Eng. 57, 2600–2604 (2010).

Kolmogorov, V.

V. Kolmogorov and R. Zabin, “What energy functions can be minimized via graph cuts?” IEEE Trans. Pattern Anal. Machine Intell. 26, 147–159 (2004).
[CrossRef]

Kumar, G. K.

G. K. Kumar, G. V. R. Prasad, and G. Mamatha, “Automatic object searching system based on real time SIFT algorithm,” in 2010 IEEE International Conference on Communication, Control, and Computing Technologies (ICCCCT) (IEEE, 2010), pp. 617–622.

Li, H.

X. Yang, H. Li, and X. Zhou, “Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and Kalman filter in time-lapse microscopy,” IEEE Trans. Circuits Syst. 53, 2405–2414 (2006).
[CrossRef]

Li, K.

K. Li and S. Zhou, “A fast SIFT feature matching algorithm for image registration,” in 2011 International Conference on Multimedia and Signal Processing (CMSP), Vol. 1 (IEEE, 2011), pp. 89–93.

Liu, S.

J. Chen and S. Liu, “A medical image segmentation method based on watershed transform,” The Fifth International Conference on Computer and Information Technology (IEEE, 2005), pp. 634–638.

Lowe, D. G.

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004).
[CrossRef]

Lu, Y.

J. Qiu, Y. Lu, T. Huang, and T. Ikenaga, “An FPGA-based real-time hardware accelerator for orientation calculation part in SIFT,” in Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009, IIH-MSP ’09 (IEEE, 2009), pp. 1334–1337.

Majeed, H.

C. Naz, H. Majeed, and H. Irshad, “Image segmentation using fuzzy clustering: a survey,” in Proceedings of IEEE Conference on ICET (IEEE, 2010), pp. 181–184.

Mamatha, G.

G. K. Kumar, G. V. R. Prasad, and G. Mamatha, “Automatic object searching system based on real time SIFT algorithm,” in 2010 IEEE International Conference on Communication, Control, and Computing Technologies (ICCCCT) (IEEE, 2010), pp. 617–622.

Mashor, M.

M. Osman, M. Mashor, H. Jaafar, R. Raof, and N. Harun, “Performance comparison between RGB and HSI linear stretching for tuberculosis bacilli detection in Ziehl-Neelsen tissue slide images,” in Proceedings of IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (IEEE, 2010), pp. 357–362.

McDonald, J.

McElhinney, C.

Naughton, T.

Naz, C.

C. Naz, H. Majeed, and H. Irshad, “Image segmentation using fuzzy clustering: a survey,” in Proceedings of IEEE Conference on ICET (IEEE, 2010), pp. 181–184.

Osman, M.

M. Osman, M. Mashor, H. Jaafar, R. Raof, and N. Harun, “Performance comparison between RGB and HSI linear stretching for tuberculosis bacilli detection in Ziehl-Neelsen tissue slide images,” in Proceedings of IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (IEEE, 2010), pp. 357–362.

Page, V.

Prasad, G. V. R.

G. K. Kumar, G. V. R. Prasad, and G. Mamatha, “Automatic object searching system based on real time SIFT algorithm,” in 2010 IEEE International Conference on Communication, Control, and Computing Technologies (ICCCCT) (IEEE, 2010), pp. 617–622.

Qiu, J.

J. Qiu, Y. Lu, T. Huang, and T. Ikenaga, “An FPGA-based real-time hardware accelerator for orientation calculation part in SIFT,” in Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009, IIH-MSP ’09 (IEEE, 2009), pp. 1334–1337.

Raof, R.

M. Osman, M. Mashor, H. Jaafar, R. Raof, and N. Harun, “Performance comparison between RGB and HSI linear stretching for tuberculosis bacilli detection in Ziehl-Neelsen tissue slide images,” in Proceedings of IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (IEEE, 2010), pp. 357–362.

Refregier, P.

Riddell, C.

C. Riddell, P. Brigger, R. Carson, and S. Bacharach, “The watershed algorithm: a method to segment noisy PET transmission images,” IEEE Trans. Nucl. Sci. 46, 713–719 (2002).
[CrossRef]

Soille, P.

P. Soille, Morphological Image Analysis: Principles and Application (Springer-Verlag, 2003).

Sonka, M.

M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision (Thomson, 2008).

Woods, R. E.

R. C. Gonzalez and R. E. Woods, Digital Imaging Processing (Prentice-Hall, 2002).

Yang, X.

X. Yang, H. Li, and X. Zhou, “Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and Kalman filter in time-lapse microscopy,” IEEE Trans. Circuits Syst. 53, 2405–2414 (2006).
[CrossRef]

Zabin, R.

V. Kolmogorov and R. Zabin, “What energy functions can be minimized via graph cuts?” IEEE Trans. Pattern Anal. Machine Intell. 26, 147–159 (2004).
[CrossRef]

Zhou, S.

K. Li and S. Zhou, “A fast SIFT feature matching algorithm for image registration,” in 2011 International Conference on Multimedia and Signal Processing (CMSP), Vol. 1 (IEEE, 2011), pp. 89–93.

Zhou, X.

X. Yang, H. Li, and X. Zhou, “Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and Kalman filter in time-lapse microscopy,” IEEE Trans. Circuits Syst. 53, 2405–2414 (2006).
[CrossRef]

IEEE Trans. Biomed. Eng.

C. Jung and C. Kim, “Segmentation clustered nuclei using H-minima transform-based marker extraction and contour parameterization,” IEEE Trans. Biomed. Eng. 57, 2600–2604 (2010).

IEEE Trans. Circuits Syst.

X. Yang, H. Li, and X. Zhou, “Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and Kalman filter in time-lapse microscopy,” IEEE Trans. Circuits Syst. 53, 2405–2414 (2006).
[CrossRef]

IEEE Trans. Image Process.

I. Karoui, R. Fablet, J. Boucher, and J. Augustin, “Variational region-based segmentation using multiple texture statistics,” IEEE Trans. Image Process. 19, 3146–3156 (2010).

IEEE Trans. Nucl. Sci.

C. Riddell, P. Brigger, R. Carson, and S. Bacharach, “The watershed algorithm: a method to segment noisy PET transmission images,” IEEE Trans. Nucl. Sci. 46, 713–719 (2002).
[CrossRef]

IEEE Trans. Pattern Anal. Machine Intell.

V. Kolmogorov and R. Zabin, “What energy functions can be minimized via graph cuts?” IEEE Trans. Pattern Anal. Machine Intell. 26, 147–159 (2004).
[CrossRef]

C. Chesnaud, P. Refregier, and V. Boulet, “Statistical region snake-based segmentation adapted to different physical noise models,” IEEE Trans. Pattern Anal. Machine Intell. 21, 1145–1157 (1999).

Int. J. Comput. Vis.

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004).
[CrossRef]

Opt. Express

Opt. Lett.

Other

C. Naz, H. Majeed, and H. Irshad, “Image segmentation using fuzzy clustering: a survey,” in Proceedings of IEEE Conference on ICET (IEEE, 2010), pp. 181–184.

A. Fabijanska, “A survey of thresholding algorithms on yarn images,” in 2010 Proceedings of VIth Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH) (IEEE, 2010), pp. 23–28.

R. C. Gonzalez and R. E. Woods, Digital Imaging Processing (Prentice-Hall, 2002).

P. Soille, Morphological Image Analysis: Principles and Application (Springer-Verlag, 2003).

M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision (Thomson, 2008).

MathWorks, Image Processing Toolbox, “Marker-controlled watershed segmentation,” http://www.mathworks.co.kr/products/image/demos.html?file=/products/demos/shipping/images/ipexwatershed.html .

G. K. Kumar, G. V. R. Prasad, and G. Mamatha, “Automatic object searching system based on real time SIFT algorithm,” in 2010 IEEE International Conference on Communication, Control, and Computing Technologies (ICCCCT) (IEEE, 2010), pp. 617–622.

J. Qiu, Y. Lu, T. Huang, and T. Ikenaga, “An FPGA-based real-time hardware accelerator for orientation calculation part in SIFT,” in Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009, IIH-MSP ’09 (IEEE, 2009), pp. 1334–1337.

K. Li and S. Zhou, “A fast SIFT feature matching algorithm for image registration,” in 2011 International Conference on Multimedia and Signal Processing (CMSP), Vol. 1 (IEEE, 2011), pp. 89–93.

D. Lowe, “Demo software: SIFT Keypoint Detector,” http://www.cs.ubc.ca/~lowe/keypoints .

X. Guo and B. Guo, “Color image morphology based on distance in the HSI color space,” in ISECS International Colloquium on Computing, Communication, Control, and Management, Vol. 3 (IEEE, 2009), pp. 264–267.

M. Osman, M. Mashor, H. Jaafar, R. Raof, and N. Harun, “Performance comparison between RGB and HSI linear stretching for tuberculosis bacilli detection in Ziehl-Neelsen tissue slide images,” in Proceedings of IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (IEEE, 2010), pp. 357–362.

Z. Cheng and R. Chen, “License plate location method based on modified HSI model of color image,” in International Conference on Electronic Measurement & Instruments, ICEMI ’09 (IEEE, 2009), pp. 4197–4201.

J. Chen and S. Liu, “A medical image segmentation method based on watershed transform,” The Fifth International Conference on Computer and Information Technology (IEEE, 2005), pp. 634–638.

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Figures (13)

Fig. 1.
Fig. 1.

(a) RGB color model (b) HSI color model.

Fig. 2.
Fig. 2.

Schematic diagram of watershed algorithm (one-dimensional example).

Fig. 3.
Fig. 3.

Flow chart of the segmentation procedure.

Fig. 4.
Fig. 4.

Flow chart of tree diameter calculation.

Fig. 5.
Fig. 5.

Flow chart of true tree diameter calculation.

Fig. 6.
Fig. 6.

Captured images of (a) left image and (b) right image.

Fig. 7.
Fig. 7.

Images in HSI color space: (a) hue, (b) saturation, and (c) intensity.

Fig. 8.
Fig. 8.

(a) Histogram of hue component and (b) image with the largest cluster after k-means.

Fig. 9.
Fig. 9.

(a) Internal markers, (b) distance-transformed image, and (c) external markers.

Fig. 10.
Fig. 10.

(a) Gradient image and (b) modified gradient image.

Fig. 11.
Fig. 11.

Segmentation results of (a) left image and (b) right image.

Fig. 12.
Fig. 12.

Segmentation results by other methods [only image in Fig. 6(a)]. (a) Segmentation result via the classical method, (b) segmentation result via the proposed method based on RBG color space, (c) segmentation result via the dual thresholding method, and (d) segmentation result with region growing method.

Fig. 13.
Fig. 13.

Matching points (a) without constraints and (b) with matching constraints.

Equations (4)

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

{H={θifBG2πθifB>G,S=13×[min(R,G,B)]R+G+B,I=R+G+B3,θ=arccos0.5[(RG)+(RB)](RG)2+(RB)(GB).
d(diameter)=4×Areaπ.
z(depth)=bfd,
scale=depthf=bf/df=bd.

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