Abstract

We present a new method for automatic adjustment of camera exposure time for visual-servo systems. The proposed method can improve the robustness of image processing in a high-dynamic-range environment. In this paper, we evaluate an appropriate exposure time by computing the local gradient information of a target area, allowing a camera to capture images without losing target features under artificially adjusted illumination conditions. To validate the advantage of the proposed method, an evaluation is made using an off-the-shelf visual-servo system with a machine vision camera. The experimental results demonstrate the effectiveness of our method, which improves the performance of the target recognition algorithm.

© 2019 Optical Society of America

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References

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  1. Q. Gu, A. AI Noman, T. Aoyama, and T. Takaki, “A fast color tracking system with automatic exposure control,” in IEEE International Conference on Information & Automation (IEEE, 2014).
  2. X. Li and X. Sui, “Colorized linear CCD data acquisition system with automatic exposure control,” Proc. SPIE 9296, 92960E (2014).
    [Crossref]
  3. Y. Su and C. C. J. Kuo, “Fast and robust camera’s auto exposure control using convex or concave model,” in IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, 2015, pp. 13–14.
  4. D. Albani, A. Youssef, V. Suriani, D. Nardi, and D. D. Bloisi, “A deep learning approach for object recognition with NAO soccer robots,” in RoboCup 2016: Robot World Cup XX., Lecture Notes in Computer Science, vol 9776 (Springer, Cham2017).
  5. Z. H. Guo, Y. B. Gu, and H. T. Yao, “Auto-exposure algorithm based on luminance histogram and region segmentation,” Appl. Mech. Mater. 543, 2278–2282 (2014).
    [Crossref]
  6. S. Inwook, O. Tae-Hyun, L. Joon-Young, C. Jinwook, C. Dong-Geol, and K. In So, “Gradient-based camera exposure control for outdoor mobile platforms,” in IEEE Transactions on Circuits and Systems for Video Technology (2018), pp. 1.
  7. S. Shimizu, “A new algorithm for exposure control based on fuzzy logic for video cameras,” IEEE Trans. Consum. Electron. 38, 617–623 (1992).
    [Crossref]
  8. J. S. Lee, Y. Y. Jung, B. S. Kim, and S. J. Ko, “An advanced video camera system with robust AF, AE, and AWB control,” IEEE Trans. Consum. Electron. 47, 694–699 (2001).
    [Crossref]
  9. J. Torres and J. M. Menéndez, “Optimal camera exposure for video surveillance systems by predictive control of shutter speed, aperture, and gain,” Proc. SPIE 9400, 94000S (2015).
    [Crossref]
  10. A. Morimura, K. Uomori, Y. Kitamura, A. Fujioka, J. Harada, S. Iwamura, and M. Hirota, “A digital video camera system,” IEEE Trans. Consum. Electron. 36, 3866–3876 (1990).
    [Crossref]
  11. W. C. Kao, L. W. Cheng, C. Y. Chien, and W. K. Lin, “Robust brightness measurement and exposure control in real-time video recording,” IEEE Trans. Instrum. Meas. 60, 1206–1216 (2011).
    [Crossref]
  12. D. Liu, J. Gu, Y. Hitomi, M. Gupta, T. Mitsunaga, and S. K. Nayar, “Efficient space-time sampling with pixel-wise coded exposure for high-speed imaging,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 248–260 (2014).
    [Crossref]
  13. Y. Su, J. Y. Lin, and C.-C. J. Kuo, “A model-based approach to camera’s auto exposure control,” J. Vis. Commun. Image Represent. 36, 122–129 (2016).
    [Crossref]
  14. T. Chen and A. El Gamal, “Optimal scheduling of capture times in a multiple-capture imaging system,” Proc. SPIE 4669, 288–296 (2002).
    [Crossref]
  15. J. Ning, T. Lu, L. Liu, L. Guo, and X. Jin, “The optimization and implementation of the auto-exposure algorithm based on image entropy,” in 8th International Congress on Image and Signal Processing (CISP), Shenyang, 2015, pp. 1020–1025.
  16. C. Zhang, Z. You, and S. Yu, “An automatic exposure algorithm based on information entropy,” in International Symposium on Instrumentation & Control Technology: Signal Analysis (2006).
  17. T. Li, Y. Song, and T. Mei, “An auto exposure control algorithm based on lane recognition for on-board camera,” in IEEE Intelligent Vehicles Symposium (IV), Seoul, 2015, pp. 851–856.
  18. K. D. Kuhnert, D. Nguyen, and L. Kuhnert, “Multiple templates auto exposure control based on luminance histogram for on-board camera,” in IEEE International Conference on Computer Science and Automation Engineering, Shanghai, 2011, pp. 237–241.
  19. H. Lu, H. Zhang, S. Yang, and Z. Zheng, “Camera parameters auto-adjusting technique for robust robot vision,” in IEEE International Conference on Robotics and Automation, Anchorage, AK, 2010, pp. 1518–1523.
  20. I. Shim, J.-Y. Lee, and I. S. Kweon, “Auto-adjusting camera exposure for outdoor robotics using gradient information,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (2014).
  21. Z. Zhang, C. Forster, and D. Scaramuzza, “Active exposure control for robust visual odometry in HDR environments,” in IEEE International Conference on Robotics & Automation (IEEE, 2017).
  22. P. E. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” in Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, 1997, pp. 369–378.
  23. U. Shin, J. Park, G. Shim, F. Rameau, and I. S. Kweon, “Camera exposure control for robust robot vision with noise-aware image quality assessment,” arXiv:1907.12646 (2019).
  24. D. G. Lowe, “Object recognition from local scale-invariant features,” in IEEE International Conference on Computer Vision (ICCV) (1999).
  25. L. Ding and A. Goshtasby, “On the Canny edge detector,” Pattern Recogn. 34, 721–725 (2001).
    [Crossref]
  26. Y. Y. Zhang, Q. H. Zeng, J. Y. Liu, Y. N. Li, and S. Liu, “An improved image edge detection algorithm based on Canny algorithm,” Navigation Control 18, 84–90 (2019).
  27. F. Yoshikawa, K. Toraichi, K. Wada, N. Ostu, H. Nakai, M. Mitsumoto, and K. Katagishi, “On a grading system for beef marbling,” Pattern Recogn. Lett. 21, 1037–1050 (2000).
    [Crossref]
  28. Y. F. Xie, X. M. Yang, X. L. Liu, and S. H. Wang, “Correlation filter tracking algorithm based on background image information fusion and multi-feature compression,” Navigation Control 18, 97–104 (2019).

2019 (2)

Y. Y. Zhang, Q. H. Zeng, J. Y. Liu, Y. N. Li, and S. Liu, “An improved image edge detection algorithm based on Canny algorithm,” Navigation Control 18, 84–90 (2019).

Y. F. Xie, X. M. Yang, X. L. Liu, and S. H. Wang, “Correlation filter tracking algorithm based on background image information fusion and multi-feature compression,” Navigation Control 18, 97–104 (2019).

2016 (1)

Y. Su, J. Y. Lin, and C.-C. J. Kuo, “A model-based approach to camera’s auto exposure control,” J. Vis. Commun. Image Represent. 36, 122–129 (2016).
[Crossref]

2015 (1)

J. Torres and J. M. Menéndez, “Optimal camera exposure for video surveillance systems by predictive control of shutter speed, aperture, and gain,” Proc. SPIE 9400, 94000S (2015).
[Crossref]

2014 (3)

X. Li and X. Sui, “Colorized linear CCD data acquisition system with automatic exposure control,” Proc. SPIE 9296, 92960E (2014).
[Crossref]

Z. H. Guo, Y. B. Gu, and H. T. Yao, “Auto-exposure algorithm based on luminance histogram and region segmentation,” Appl. Mech. Mater. 543, 2278–2282 (2014).
[Crossref]

D. Liu, J. Gu, Y. Hitomi, M. Gupta, T. Mitsunaga, and S. K. Nayar, “Efficient space-time sampling with pixel-wise coded exposure for high-speed imaging,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 248–260 (2014).
[Crossref]

2011 (1)

W. C. Kao, L. W. Cheng, C. Y. Chien, and W. K. Lin, “Robust brightness measurement and exposure control in real-time video recording,” IEEE Trans. Instrum. Meas. 60, 1206–1216 (2011).
[Crossref]

2002 (1)

T. Chen and A. El Gamal, “Optimal scheduling of capture times in a multiple-capture imaging system,” Proc. SPIE 4669, 288–296 (2002).
[Crossref]

2001 (2)

L. Ding and A. Goshtasby, “On the Canny edge detector,” Pattern Recogn. 34, 721–725 (2001).
[Crossref]

J. S. Lee, Y. Y. Jung, B. S. Kim, and S. J. Ko, “An advanced video camera system with robust AF, AE, and AWB control,” IEEE Trans. Consum. Electron. 47, 694–699 (2001).
[Crossref]

2000 (1)

F. Yoshikawa, K. Toraichi, K. Wada, N. Ostu, H. Nakai, M. Mitsumoto, and K. Katagishi, “On a grading system for beef marbling,” Pattern Recogn. Lett. 21, 1037–1050 (2000).
[Crossref]

1992 (1)

S. Shimizu, “A new algorithm for exposure control based on fuzzy logic for video cameras,” IEEE Trans. Consum. Electron. 38, 617–623 (1992).
[Crossref]

1990 (1)

A. Morimura, K. Uomori, Y. Kitamura, A. Fujioka, J. Harada, S. Iwamura, and M. Hirota, “A digital video camera system,” IEEE Trans. Consum. Electron. 36, 3866–3876 (1990).
[Crossref]

AI Noman, A.

Q. Gu, A. AI Noman, T. Aoyama, and T. Takaki, “A fast color tracking system with automatic exposure control,” in IEEE International Conference on Information & Automation (IEEE, 2014).

Albani, D.

D. Albani, A. Youssef, V. Suriani, D. Nardi, and D. D. Bloisi, “A deep learning approach for object recognition with NAO soccer robots,” in RoboCup 2016: Robot World Cup XX., Lecture Notes in Computer Science, vol 9776 (Springer, Cham2017).

Aoyama, T.

Q. Gu, A. AI Noman, T. Aoyama, and T. Takaki, “A fast color tracking system with automatic exposure control,” in IEEE International Conference on Information & Automation (IEEE, 2014).

Bloisi, D. D.

D. Albani, A. Youssef, V. Suriani, D. Nardi, and D. D. Bloisi, “A deep learning approach for object recognition with NAO soccer robots,” in RoboCup 2016: Robot World Cup XX., Lecture Notes in Computer Science, vol 9776 (Springer, Cham2017).

Chen, T.

T. Chen and A. El Gamal, “Optimal scheduling of capture times in a multiple-capture imaging system,” Proc. SPIE 4669, 288–296 (2002).
[Crossref]

Cheng, L. W.

W. C. Kao, L. W. Cheng, C. Y. Chien, and W. K. Lin, “Robust brightness measurement and exposure control in real-time video recording,” IEEE Trans. Instrum. Meas. 60, 1206–1216 (2011).
[Crossref]

Chien, C. Y.

W. C. Kao, L. W. Cheng, C. Y. Chien, and W. K. Lin, “Robust brightness measurement and exposure control in real-time video recording,” IEEE Trans. Instrum. Meas. 60, 1206–1216 (2011).
[Crossref]

Debevec, P. E.

P. E. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” in Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, 1997, pp. 369–378.

Ding, L.

L. Ding and A. Goshtasby, “On the Canny edge detector,” Pattern Recogn. 34, 721–725 (2001).
[Crossref]

Dong-Geol, C.

S. Inwook, O. Tae-Hyun, L. Joon-Young, C. Jinwook, C. Dong-Geol, and K. In So, “Gradient-based camera exposure control for outdoor mobile platforms,” in IEEE Transactions on Circuits and Systems for Video Technology (2018), pp. 1.

El Gamal, A.

T. Chen and A. El Gamal, “Optimal scheduling of capture times in a multiple-capture imaging system,” Proc. SPIE 4669, 288–296 (2002).
[Crossref]

Forster, C.

Z. Zhang, C. Forster, and D. Scaramuzza, “Active exposure control for robust visual odometry in HDR environments,” in IEEE International Conference on Robotics & Automation (IEEE, 2017).

Fujioka, A.

A. Morimura, K. Uomori, Y. Kitamura, A. Fujioka, J. Harada, S. Iwamura, and M. Hirota, “A digital video camera system,” IEEE Trans. Consum. Electron. 36, 3866–3876 (1990).
[Crossref]

Goshtasby, A.

L. Ding and A. Goshtasby, “On the Canny edge detector,” Pattern Recogn. 34, 721–725 (2001).
[Crossref]

Gu, J.

D. Liu, J. Gu, Y. Hitomi, M. Gupta, T. Mitsunaga, and S. K. Nayar, “Efficient space-time sampling with pixel-wise coded exposure for high-speed imaging,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 248–260 (2014).
[Crossref]

Gu, Q.

Q. Gu, A. AI Noman, T. Aoyama, and T. Takaki, “A fast color tracking system with automatic exposure control,” in IEEE International Conference on Information & Automation (IEEE, 2014).

Gu, Y. B.

Z. H. Guo, Y. B. Gu, and H. T. Yao, “Auto-exposure algorithm based on luminance histogram and region segmentation,” Appl. Mech. Mater. 543, 2278–2282 (2014).
[Crossref]

Guo, L.

J. Ning, T. Lu, L. Liu, L. Guo, and X. Jin, “The optimization and implementation of the auto-exposure algorithm based on image entropy,” in 8th International Congress on Image and Signal Processing (CISP), Shenyang, 2015, pp. 1020–1025.

Guo, Z. H.

Z. H. Guo, Y. B. Gu, and H. T. Yao, “Auto-exposure algorithm based on luminance histogram and region segmentation,” Appl. Mech. Mater. 543, 2278–2282 (2014).
[Crossref]

Gupta, M.

D. Liu, J. Gu, Y. Hitomi, M. Gupta, T. Mitsunaga, and S. K. Nayar, “Efficient space-time sampling with pixel-wise coded exposure for high-speed imaging,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 248–260 (2014).
[Crossref]

Harada, J.

A. Morimura, K. Uomori, Y. Kitamura, A. Fujioka, J. Harada, S. Iwamura, and M. Hirota, “A digital video camera system,” IEEE Trans. Consum. Electron. 36, 3866–3876 (1990).
[Crossref]

Hirota, M.

A. Morimura, K. Uomori, Y. Kitamura, A. Fujioka, J. Harada, S. Iwamura, and M. Hirota, “A digital video camera system,” IEEE Trans. Consum. Electron. 36, 3866–3876 (1990).
[Crossref]

Hitomi, Y.

D. Liu, J. Gu, Y. Hitomi, M. Gupta, T. Mitsunaga, and S. K. Nayar, “Efficient space-time sampling with pixel-wise coded exposure for high-speed imaging,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 248–260 (2014).
[Crossref]

In So, K.

S. Inwook, O. Tae-Hyun, L. Joon-Young, C. Jinwook, C. Dong-Geol, and K. In So, “Gradient-based camera exposure control for outdoor mobile platforms,” in IEEE Transactions on Circuits and Systems for Video Technology (2018), pp. 1.

Inwook, S.

S. Inwook, O. Tae-Hyun, L. Joon-Young, C. Jinwook, C. Dong-Geol, and K. In So, “Gradient-based camera exposure control for outdoor mobile platforms,” in IEEE Transactions on Circuits and Systems for Video Technology (2018), pp. 1.

Iwamura, S.

A. Morimura, K. Uomori, Y. Kitamura, A. Fujioka, J. Harada, S. Iwamura, and M. Hirota, “A digital video camera system,” IEEE Trans. Consum. Electron. 36, 3866–3876 (1990).
[Crossref]

Jin, X.

J. Ning, T. Lu, L. Liu, L. Guo, and X. Jin, “The optimization and implementation of the auto-exposure algorithm based on image entropy,” in 8th International Congress on Image and Signal Processing (CISP), Shenyang, 2015, pp. 1020–1025.

Jinwook, C.

S. Inwook, O. Tae-Hyun, L. Joon-Young, C. Jinwook, C. Dong-Geol, and K. In So, “Gradient-based camera exposure control for outdoor mobile platforms,” in IEEE Transactions on Circuits and Systems for Video Technology (2018), pp. 1.

Joon-Young, L.

S. Inwook, O. Tae-Hyun, L. Joon-Young, C. Jinwook, C. Dong-Geol, and K. In So, “Gradient-based camera exposure control for outdoor mobile platforms,” in IEEE Transactions on Circuits and Systems for Video Technology (2018), pp. 1.

Jung, Y. Y.

J. S. Lee, Y. Y. Jung, B. S. Kim, and S. J. Ko, “An advanced video camera system with robust AF, AE, and AWB control,” IEEE Trans. Consum. Electron. 47, 694–699 (2001).
[Crossref]

Kao, W. C.

W. C. Kao, L. W. Cheng, C. Y. Chien, and W. K. Lin, “Robust brightness measurement and exposure control in real-time video recording,” IEEE Trans. Instrum. Meas. 60, 1206–1216 (2011).
[Crossref]

Katagishi, K.

F. Yoshikawa, K. Toraichi, K. Wada, N. Ostu, H. Nakai, M. Mitsumoto, and K. Katagishi, “On a grading system for beef marbling,” Pattern Recogn. Lett. 21, 1037–1050 (2000).
[Crossref]

Kim, B. S.

J. S. Lee, Y. Y. Jung, B. S. Kim, and S. J. Ko, “An advanced video camera system with robust AF, AE, and AWB control,” IEEE Trans. Consum. Electron. 47, 694–699 (2001).
[Crossref]

Kitamura, Y.

A. Morimura, K. Uomori, Y. Kitamura, A. Fujioka, J. Harada, S. Iwamura, and M. Hirota, “A digital video camera system,” IEEE Trans. Consum. Electron. 36, 3866–3876 (1990).
[Crossref]

Ko, S. J.

J. S. Lee, Y. Y. Jung, B. S. Kim, and S. J. Ko, “An advanced video camera system with robust AF, AE, and AWB control,” IEEE Trans. Consum. Electron. 47, 694–699 (2001).
[Crossref]

Kuhnert, K. D.

K. D. Kuhnert, D. Nguyen, and L. Kuhnert, “Multiple templates auto exposure control based on luminance histogram for on-board camera,” in IEEE International Conference on Computer Science and Automation Engineering, Shanghai, 2011, pp. 237–241.

Kuhnert, L.

K. D. Kuhnert, D. Nguyen, and L. Kuhnert, “Multiple templates auto exposure control based on luminance histogram for on-board camera,” in IEEE International Conference on Computer Science and Automation Engineering, Shanghai, 2011, pp. 237–241.

Kuo, C. C. J.

Y. Su and C. C. J. Kuo, “Fast and robust camera’s auto exposure control using convex or concave model,” in IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, 2015, pp. 13–14.

Kuo, C.-C. J.

Y. Su, J. Y. Lin, and C.-C. J. Kuo, “A model-based approach to camera’s auto exposure control,” J. Vis. Commun. Image Represent. 36, 122–129 (2016).
[Crossref]

Kweon, I. S.

U. Shin, J. Park, G. Shim, F. Rameau, and I. S. Kweon, “Camera exposure control for robust robot vision with noise-aware image quality assessment,” arXiv:1907.12646 (2019).

I. Shim, J.-Y. Lee, and I. S. Kweon, “Auto-adjusting camera exposure for outdoor robotics using gradient information,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (2014).

Lee, J. S.

J. S. Lee, Y. Y. Jung, B. S. Kim, and S. J. Ko, “An advanced video camera system with robust AF, AE, and AWB control,” IEEE Trans. Consum. Electron. 47, 694–699 (2001).
[Crossref]

Lee, J.-Y.

I. Shim, J.-Y. Lee, and I. S. Kweon, “Auto-adjusting camera exposure for outdoor robotics using gradient information,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (2014).

Li, T.

T. Li, Y. Song, and T. Mei, “An auto exposure control algorithm based on lane recognition for on-board camera,” in IEEE Intelligent Vehicles Symposium (IV), Seoul, 2015, pp. 851–856.

Li, X.

X. Li and X. Sui, “Colorized linear CCD data acquisition system with automatic exposure control,” Proc. SPIE 9296, 92960E (2014).
[Crossref]

Li, Y. N.

Y. Y. Zhang, Q. H. Zeng, J. Y. Liu, Y. N. Li, and S. Liu, “An improved image edge detection algorithm based on Canny algorithm,” Navigation Control 18, 84–90 (2019).

Lin, J. Y.

Y. Su, J. Y. Lin, and C.-C. J. Kuo, “A model-based approach to camera’s auto exposure control,” J. Vis. Commun. Image Represent. 36, 122–129 (2016).
[Crossref]

Lin, W. K.

W. C. Kao, L. W. Cheng, C. Y. Chien, and W. K. Lin, “Robust brightness measurement and exposure control in real-time video recording,” IEEE Trans. Instrum. Meas. 60, 1206–1216 (2011).
[Crossref]

Liu, D.

D. Liu, J. Gu, Y. Hitomi, M. Gupta, T. Mitsunaga, and S. K. Nayar, “Efficient space-time sampling with pixel-wise coded exposure for high-speed imaging,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 248–260 (2014).
[Crossref]

Liu, J. Y.

Y. Y. Zhang, Q. H. Zeng, J. Y. Liu, Y. N. Li, and S. Liu, “An improved image edge detection algorithm based on Canny algorithm,” Navigation Control 18, 84–90 (2019).

Liu, L.

J. Ning, T. Lu, L. Liu, L. Guo, and X. Jin, “The optimization and implementation of the auto-exposure algorithm based on image entropy,” in 8th International Congress on Image and Signal Processing (CISP), Shenyang, 2015, pp. 1020–1025.

Liu, S.

Y. Y. Zhang, Q. H. Zeng, J. Y. Liu, Y. N. Li, and S. Liu, “An improved image edge detection algorithm based on Canny algorithm,” Navigation Control 18, 84–90 (2019).

Liu, X. L.

Y. F. Xie, X. M. Yang, X. L. Liu, and S. H. Wang, “Correlation filter tracking algorithm based on background image information fusion and multi-feature compression,” Navigation Control 18, 97–104 (2019).

Lowe, D. G.

D. G. Lowe, “Object recognition from local scale-invariant features,” in IEEE International Conference on Computer Vision (ICCV) (1999).

Lu, H.

H. Lu, H. Zhang, S. Yang, and Z. Zheng, “Camera parameters auto-adjusting technique for robust robot vision,” in IEEE International Conference on Robotics and Automation, Anchorage, AK, 2010, pp. 1518–1523.

Lu, T.

J. Ning, T. Lu, L. Liu, L. Guo, and X. Jin, “The optimization and implementation of the auto-exposure algorithm based on image entropy,” in 8th International Congress on Image and Signal Processing (CISP), Shenyang, 2015, pp. 1020–1025.

Malik, J.

P. E. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” in Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, 1997, pp. 369–378.

Mei, T.

T. Li, Y. Song, and T. Mei, “An auto exposure control algorithm based on lane recognition for on-board camera,” in IEEE Intelligent Vehicles Symposium (IV), Seoul, 2015, pp. 851–856.

Menéndez, J. M.

J. Torres and J. M. Menéndez, “Optimal camera exposure for video surveillance systems by predictive control of shutter speed, aperture, and gain,” Proc. SPIE 9400, 94000S (2015).
[Crossref]

Mitsumoto, M.

F. Yoshikawa, K. Toraichi, K. Wada, N. Ostu, H. Nakai, M. Mitsumoto, and K. Katagishi, “On a grading system for beef marbling,” Pattern Recogn. Lett. 21, 1037–1050 (2000).
[Crossref]

Mitsunaga, T.

D. Liu, J. Gu, Y. Hitomi, M. Gupta, T. Mitsunaga, and S. K. Nayar, “Efficient space-time sampling with pixel-wise coded exposure for high-speed imaging,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 248–260 (2014).
[Crossref]

Morimura, A.

A. Morimura, K. Uomori, Y. Kitamura, A. Fujioka, J. Harada, S. Iwamura, and M. Hirota, “A digital video camera system,” IEEE Trans. Consum. Electron. 36, 3866–3876 (1990).
[Crossref]

Nakai, H.

F. Yoshikawa, K. Toraichi, K. Wada, N. Ostu, H. Nakai, M. Mitsumoto, and K. Katagishi, “On a grading system for beef marbling,” Pattern Recogn. Lett. 21, 1037–1050 (2000).
[Crossref]

Nardi, D.

D. Albani, A. Youssef, V. Suriani, D. Nardi, and D. D. Bloisi, “A deep learning approach for object recognition with NAO soccer robots,” in RoboCup 2016: Robot World Cup XX., Lecture Notes in Computer Science, vol 9776 (Springer, Cham2017).

Nayar, S. K.

D. Liu, J. Gu, Y. Hitomi, M. Gupta, T. Mitsunaga, and S. K. Nayar, “Efficient space-time sampling with pixel-wise coded exposure for high-speed imaging,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 248–260 (2014).
[Crossref]

Nguyen, D.

K. D. Kuhnert, D. Nguyen, and L. Kuhnert, “Multiple templates auto exposure control based on luminance histogram for on-board camera,” in IEEE International Conference on Computer Science and Automation Engineering, Shanghai, 2011, pp. 237–241.

Ning, J.

J. Ning, T. Lu, L. Liu, L. Guo, and X. Jin, “The optimization and implementation of the auto-exposure algorithm based on image entropy,” in 8th International Congress on Image and Signal Processing (CISP), Shenyang, 2015, pp. 1020–1025.

Ostu, N.

F. Yoshikawa, K. Toraichi, K. Wada, N. Ostu, H. Nakai, M. Mitsumoto, and K. Katagishi, “On a grading system for beef marbling,” Pattern Recogn. Lett. 21, 1037–1050 (2000).
[Crossref]

Park, J.

U. Shin, J. Park, G. Shim, F. Rameau, and I. S. Kweon, “Camera exposure control for robust robot vision with noise-aware image quality assessment,” arXiv:1907.12646 (2019).

Rameau, F.

U. Shin, J. Park, G. Shim, F. Rameau, and I. S. Kweon, “Camera exposure control for robust robot vision with noise-aware image quality assessment,” arXiv:1907.12646 (2019).

Scaramuzza, D.

Z. Zhang, C. Forster, and D. Scaramuzza, “Active exposure control for robust visual odometry in HDR environments,” in IEEE International Conference on Robotics & Automation (IEEE, 2017).

Shim, G.

U. Shin, J. Park, G. Shim, F. Rameau, and I. S. Kweon, “Camera exposure control for robust robot vision with noise-aware image quality assessment,” arXiv:1907.12646 (2019).

Shim, I.

I. Shim, J.-Y. Lee, and I. S. Kweon, “Auto-adjusting camera exposure for outdoor robotics using gradient information,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (2014).

Shimizu, S.

S. Shimizu, “A new algorithm for exposure control based on fuzzy logic for video cameras,” IEEE Trans. Consum. Electron. 38, 617–623 (1992).
[Crossref]

Shin, U.

U. Shin, J. Park, G. Shim, F. Rameau, and I. S. Kweon, “Camera exposure control for robust robot vision with noise-aware image quality assessment,” arXiv:1907.12646 (2019).

Song, Y.

T. Li, Y. Song, and T. Mei, “An auto exposure control algorithm based on lane recognition for on-board camera,” in IEEE Intelligent Vehicles Symposium (IV), Seoul, 2015, pp. 851–856.

Su, Y.

Y. Su, J. Y. Lin, and C.-C. J. Kuo, “A model-based approach to camera’s auto exposure control,” J. Vis. Commun. Image Represent. 36, 122–129 (2016).
[Crossref]

Y. Su and C. C. J. Kuo, “Fast and robust camera’s auto exposure control using convex or concave model,” in IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, 2015, pp. 13–14.

Sui, X.

X. Li and X. Sui, “Colorized linear CCD data acquisition system with automatic exposure control,” Proc. SPIE 9296, 92960E (2014).
[Crossref]

Suriani, V.

D. Albani, A. Youssef, V. Suriani, D. Nardi, and D. D. Bloisi, “A deep learning approach for object recognition with NAO soccer robots,” in RoboCup 2016: Robot World Cup XX., Lecture Notes in Computer Science, vol 9776 (Springer, Cham2017).

Tae-Hyun, O.

S. Inwook, O. Tae-Hyun, L. Joon-Young, C. Jinwook, C. Dong-Geol, and K. In So, “Gradient-based camera exposure control for outdoor mobile platforms,” in IEEE Transactions on Circuits and Systems for Video Technology (2018), pp. 1.

Takaki, T.

Q. Gu, A. AI Noman, T. Aoyama, and T. Takaki, “A fast color tracking system with automatic exposure control,” in IEEE International Conference on Information & Automation (IEEE, 2014).

Toraichi, K.

F. Yoshikawa, K. Toraichi, K. Wada, N. Ostu, H. Nakai, M. Mitsumoto, and K. Katagishi, “On a grading system for beef marbling,” Pattern Recogn. Lett. 21, 1037–1050 (2000).
[Crossref]

Torres, J.

J. Torres and J. M. Menéndez, “Optimal camera exposure for video surveillance systems by predictive control of shutter speed, aperture, and gain,” Proc. SPIE 9400, 94000S (2015).
[Crossref]

Uomori, K.

A. Morimura, K. Uomori, Y. Kitamura, A. Fujioka, J. Harada, S. Iwamura, and M. Hirota, “A digital video camera system,” IEEE Trans. Consum. Electron. 36, 3866–3876 (1990).
[Crossref]

Wada, K.

F. Yoshikawa, K. Toraichi, K. Wada, N. Ostu, H. Nakai, M. Mitsumoto, and K. Katagishi, “On a grading system for beef marbling,” Pattern Recogn. Lett. 21, 1037–1050 (2000).
[Crossref]

Wang, S. H.

Y. F. Xie, X. M. Yang, X. L. Liu, and S. H. Wang, “Correlation filter tracking algorithm based on background image information fusion and multi-feature compression,” Navigation Control 18, 97–104 (2019).

Xie, Y. F.

Y. F. Xie, X. M. Yang, X. L. Liu, and S. H. Wang, “Correlation filter tracking algorithm based on background image information fusion and multi-feature compression,” Navigation Control 18, 97–104 (2019).

Yang, S.

H. Lu, H. Zhang, S. Yang, and Z. Zheng, “Camera parameters auto-adjusting technique for robust robot vision,” in IEEE International Conference on Robotics and Automation, Anchorage, AK, 2010, pp. 1518–1523.

Yang, X. M.

Y. F. Xie, X. M. Yang, X. L. Liu, and S. H. Wang, “Correlation filter tracking algorithm based on background image information fusion and multi-feature compression,” Navigation Control 18, 97–104 (2019).

Yao, H. T.

Z. H. Guo, Y. B. Gu, and H. T. Yao, “Auto-exposure algorithm based on luminance histogram and region segmentation,” Appl. Mech. Mater. 543, 2278–2282 (2014).
[Crossref]

Yoshikawa, F.

F. Yoshikawa, K. Toraichi, K. Wada, N. Ostu, H. Nakai, M. Mitsumoto, and K. Katagishi, “On a grading system for beef marbling,” Pattern Recogn. Lett. 21, 1037–1050 (2000).
[Crossref]

You, Z.

C. Zhang, Z. You, and S. Yu, “An automatic exposure algorithm based on information entropy,” in International Symposium on Instrumentation & Control Technology: Signal Analysis (2006).

Youssef, A.

D. Albani, A. Youssef, V. Suriani, D. Nardi, and D. D. Bloisi, “A deep learning approach for object recognition with NAO soccer robots,” in RoboCup 2016: Robot World Cup XX., Lecture Notes in Computer Science, vol 9776 (Springer, Cham2017).

Yu, S.

C. Zhang, Z. You, and S. Yu, “An automatic exposure algorithm based on information entropy,” in International Symposium on Instrumentation & Control Technology: Signal Analysis (2006).

Zeng, Q. H.

Y. Y. Zhang, Q. H. Zeng, J. Y. Liu, Y. N. Li, and S. Liu, “An improved image edge detection algorithm based on Canny algorithm,” Navigation Control 18, 84–90 (2019).

Zhang, C.

C. Zhang, Z. You, and S. Yu, “An automatic exposure algorithm based on information entropy,” in International Symposium on Instrumentation & Control Technology: Signal Analysis (2006).

Zhang, H.

H. Lu, H. Zhang, S. Yang, and Z. Zheng, “Camera parameters auto-adjusting technique for robust robot vision,” in IEEE International Conference on Robotics and Automation, Anchorage, AK, 2010, pp. 1518–1523.

Zhang, Y. Y.

Y. Y. Zhang, Q. H. Zeng, J. Y. Liu, Y. N. Li, and S. Liu, “An improved image edge detection algorithm based on Canny algorithm,” Navigation Control 18, 84–90 (2019).

Zhang, Z.

Z. Zhang, C. Forster, and D. Scaramuzza, “Active exposure control for robust visual odometry in HDR environments,” in IEEE International Conference on Robotics & Automation (IEEE, 2017).

Zheng, Z.

H. Lu, H. Zhang, S. Yang, and Z. Zheng, “Camera parameters auto-adjusting technique for robust robot vision,” in IEEE International Conference on Robotics and Automation, Anchorage, AK, 2010, pp. 1518–1523.

Appl. Mech. Mater. (1)

Z. H. Guo, Y. B. Gu, and H. T. Yao, “Auto-exposure algorithm based on luminance histogram and region segmentation,” Appl. Mech. Mater. 543, 2278–2282 (2014).
[Crossref]

IEEE Trans. Consum. Electron. (3)

S. Shimizu, “A new algorithm for exposure control based on fuzzy logic for video cameras,” IEEE Trans. Consum. Electron. 38, 617–623 (1992).
[Crossref]

J. S. Lee, Y. Y. Jung, B. S. Kim, and S. J. Ko, “An advanced video camera system with robust AF, AE, and AWB control,” IEEE Trans. Consum. Electron. 47, 694–699 (2001).
[Crossref]

A. Morimura, K. Uomori, Y. Kitamura, A. Fujioka, J. Harada, S. Iwamura, and M. Hirota, “A digital video camera system,” IEEE Trans. Consum. Electron. 36, 3866–3876 (1990).
[Crossref]

IEEE Trans. Instrum. Meas. (1)

W. C. Kao, L. W. Cheng, C. Y. Chien, and W. K. Lin, “Robust brightness measurement and exposure control in real-time video recording,” IEEE Trans. Instrum. Meas. 60, 1206–1216 (2011).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

D. Liu, J. Gu, Y. Hitomi, M. Gupta, T. Mitsunaga, and S. K. Nayar, “Efficient space-time sampling with pixel-wise coded exposure for high-speed imaging,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 248–260 (2014).
[Crossref]

J. Vis. Commun. Image Represent. (1)

Y. Su, J. Y. Lin, and C.-C. J. Kuo, “A model-based approach to camera’s auto exposure control,” J. Vis. Commun. Image Represent. 36, 122–129 (2016).
[Crossref]

Navigation Control (2)

Y. Y. Zhang, Q. H. Zeng, J. Y. Liu, Y. N. Li, and S. Liu, “An improved image edge detection algorithm based on Canny algorithm,” Navigation Control 18, 84–90 (2019).

Y. F. Xie, X. M. Yang, X. L. Liu, and S. H. Wang, “Correlation filter tracking algorithm based on background image information fusion and multi-feature compression,” Navigation Control 18, 97–104 (2019).

Pattern Recogn. (1)

L. Ding and A. Goshtasby, “On the Canny edge detector,” Pattern Recogn. 34, 721–725 (2001).
[Crossref]

Pattern Recogn. Lett. (1)

F. Yoshikawa, K. Toraichi, K. Wada, N. Ostu, H. Nakai, M. Mitsumoto, and K. Katagishi, “On a grading system for beef marbling,” Pattern Recogn. Lett. 21, 1037–1050 (2000).
[Crossref]

Proc. SPIE (3)

J. Torres and J. M. Menéndez, “Optimal camera exposure for video surveillance systems by predictive control of shutter speed, aperture, and gain,” Proc. SPIE 9400, 94000S (2015).
[Crossref]

X. Li and X. Sui, “Colorized linear CCD data acquisition system with automatic exposure control,” Proc. SPIE 9296, 92960E (2014).
[Crossref]

T. Chen and A. El Gamal, “Optimal scheduling of capture times in a multiple-capture imaging system,” Proc. SPIE 4669, 288–296 (2002).
[Crossref]

Other (14)

J. Ning, T. Lu, L. Liu, L. Guo, and X. Jin, “The optimization and implementation of the auto-exposure algorithm based on image entropy,” in 8th International Congress on Image and Signal Processing (CISP), Shenyang, 2015, pp. 1020–1025.

C. Zhang, Z. You, and S. Yu, “An automatic exposure algorithm based on information entropy,” in International Symposium on Instrumentation & Control Technology: Signal Analysis (2006).

T. Li, Y. Song, and T. Mei, “An auto exposure control algorithm based on lane recognition for on-board camera,” in IEEE Intelligent Vehicles Symposium (IV), Seoul, 2015, pp. 851–856.

K. D. Kuhnert, D. Nguyen, and L. Kuhnert, “Multiple templates auto exposure control based on luminance histogram for on-board camera,” in IEEE International Conference on Computer Science and Automation Engineering, Shanghai, 2011, pp. 237–241.

H. Lu, H. Zhang, S. Yang, and Z. Zheng, “Camera parameters auto-adjusting technique for robust robot vision,” in IEEE International Conference on Robotics and Automation, Anchorage, AK, 2010, pp. 1518–1523.

I. Shim, J.-Y. Lee, and I. S. Kweon, “Auto-adjusting camera exposure for outdoor robotics using gradient information,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (2014).

Z. Zhang, C. Forster, and D. Scaramuzza, “Active exposure control for robust visual odometry in HDR environments,” in IEEE International Conference on Robotics & Automation (IEEE, 2017).

P. E. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” in Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, 1997, pp. 369–378.

U. Shin, J. Park, G. Shim, F. Rameau, and I. S. Kweon, “Camera exposure control for robust robot vision with noise-aware image quality assessment,” arXiv:1907.12646 (2019).

D. G. Lowe, “Object recognition from local scale-invariant features,” in IEEE International Conference on Computer Vision (ICCV) (1999).

Y. Su and C. C. J. Kuo, “Fast and robust camera’s auto exposure control using convex or concave model,” in IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, 2015, pp. 13–14.

D. Albani, A. Youssef, V. Suriani, D. Nardi, and D. D. Bloisi, “A deep learning approach for object recognition with NAO soccer robots,” in RoboCup 2016: Robot World Cup XX., Lecture Notes in Computer Science, vol 9776 (Springer, Cham2017).

S. Inwook, O. Tae-Hyun, L. Joon-Young, C. Jinwook, C. Dong-Geol, and K. In So, “Gradient-based camera exposure control for outdoor mobile platforms,” in IEEE Transactions on Circuits and Systems for Video Technology (2018), pp. 1.

Q. Gu, A. AI Noman, T. Aoyama, and T. Takaki, “A fast color tracking system with automatic exposure control,” in IEEE International Conference on Information & Automation (IEEE, 2014).

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Figures (12)

Fig. 1.
Fig. 1. Framework of the adaptive exposure control process.
Fig. 2.
Fig. 2. Target region detection process.
Fig. 3.
Fig. 3. Recovered inverse response function.
Fig. 4.
Fig. 4. Mapping function between the gradient information and gradient magnitude at different values of control parameters $\lambda $, $\delta $, and $p$.
Fig. 5.
Fig. 5. Images in the left column illustrate the relationship between the exposure time and metrics. Images in the right column show the best images according to each metric.
Fig. 6.
Fig. 6. Number of extracted speeded-up robust features (SURF) in the best images.
Fig. 7.
Fig. 7. RMS error. (a) Gradient information and (b) feature number at different Gaussian noise levels in the best images regarding ${M_{{\rm shim}}}$, ${M_{\rm new}}$, and ${M_{\rm softperc}}$.
Fig. 8.
Fig. 8. Time consumption in different methods.
Fig. 9.
Fig. 9. Real image sequence in the indoor environment. First column: fixed exposure time; second column: Zhang’s exposure method; third column: our method.
Fig. 10.
Fig. 10. Tracking performance in outdoor environments. First row: fixed exposure time (no frame loss); second row: Zhang’s exposure method (frame loss number: 56); third row: our method (no frame loss).
Fig. 11.
Fig. 11. Comparison of the exposure control methods: (a) indoors and (b) outdoors.
Fig. 12.
Fig. 12. Feature number of the office light sequence.

Tables (1)

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Table 1. Computation Time Analysis

Equations (13)

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Z i j = f ( X ) = f ( E i Δ t j ) .
f 1 ( Z i j ) = E i Δ t j .
g ( Z i j ) = ln f 1 ( Z i j ) = ln E i + ln Δ t j .
m ¯ u i = { 1 N log ( λ ( m u i δ ) + 1 ) f o r m u i δ 0 f o r m u i δ ,
M ( R ) = u i R m u i .
M s o f t p e r c ( p ) = i [ 0 , S ] W i th ( p ) m u i th ,
W i th ( p ) = { 1 N sin ( π 2 p S i ) k , i p S 1 N sin ( π 2 π 2 S p S i ) k , i > p S ,
M n e w ( p ) = i [ 0 , S ] , f R W i th ( p ) m f i th ,
Δ t n e x t = Δ t + M n e w ( p ) Δ t .
M n e w ( p ) Δ t = i [ 0 , S ] W i th ( p ) m f i th Δ t .
m f i Δ t = 2 R ( f , Δ t ) T Δ t [ R ( f , Δ t ) ] .
Δ t [ R ( f , Δ t ) ] = R ( f , Δ t ) Δ t .
R ( f , Δ t ) Δ t = f [ f 1 ( R ) ] E ( f ) = 1 g ( R ) Δ t .

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