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JACIII Vol.24 No.6 pp. 750-762
doi: 10.20965/jaciii.2020.p0750
(2020)

Paper:

Robust and Automatic Skyline Detection Algorithm Based on MSSDN

Fan Guo*, Yuxiang Mai**, Jin Tang*,†, Yu Huang*, and Lijun Zhu*

*School of Automation, Central South University
Changsha 410083, China

**School of Computer Science and Engineering, Central South University
Changsha 410083, China

Corresponding author

Received:
August 6, 2019
Accepted:
August 31, 2020
Published:
November 20, 2020
Keywords:
skyline detection, multi-stream-stage DenseNet, rainy streak, probability graph, dynamic programming
Abstract
Robust and Automatic Skyline Detection Algorithm Based on MSSDN

Algorithm framework for skyline detection

Automatic detection of the skyline plays an important role in several applications, such as visual geo-localization, flight control, port security, and mountain peak recognition. Existing skyline detection methods are mostly used under common weather conditions; however, they do not consider bad weather situations, such as rain, which limits their application in real scenes. In this paper, we propose a multi-stream-stage DenseNet to detect skyline automatically under different weather conditions. This model fully considers the adverse factors influencing the skyline and outputs a probability graph of the skyline. Finally, a dynamic programming algorithm is implemented to detect the skyline in images accurately. A comparison with the existing state-of-the-art methods proves that the proposed model shows a good performance under rainy or common weather conditions and exhibits the best detection precision for the public database.

Cite this article as:
Fan Guo, Yuxiang Mai, Jin Tang, Yu Huang, and Lijun Zhu, “Robust and Automatic Skyline Detection Algorithm Based on MSSDN,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.6, pp. 750-762, 2020.
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References
  1. [1] C.-C. Chiu, Y.-J. Liu, S.-Y. Chiu et al., “A skyline detection algorithm for use in different weather and environmental conditions,” Proc. of the IEEE Int. Conf. on Electro Information Technology (EIT), pp. 680-685, Grand Forks, USA, May 19-21, 2016.
  2. [2] Y.-J. Liu, C.-C. Chiu, and Y.-H. Yang, “A Robust Vision-Based Skyline Detection Algorithm Under Different Weather Conditions,” IEEE Access, Vol.5, pp. 22992-23009, 2017.
  3. [3] M. Ayadi, L. Suta, M. Scuturici, S. Miguet, and C. B. Amar, “A parametric algorithm for skyline extraction,” Proc. of the 17th Int. Conf. on Advanced Concepts for Intelligent Vision Systems, pp. 604-615, Lecce, Italy, October 24-27, 2016.
  4. [4] W.-N. Lie, T C.-I. Lin, T.-C. Lin et al., “A robust dynamic programming algorithm to extract skyline in images for navigation,” Pattern Recognition Letters, Vol.26, No.2, pp. 221-230, 2005.
  5. [5] S. Fefilatyev, V. Smarodzinava, L. O. Hall et al., “Horizon Detection Using Machine Learning Techniques,” Proc. of Int. Conf. on Machine Learning and Applications, pp. 17-21, Orlando, USA, December 14-16, 2006.
  6. [6] Y.-L. Hung, C.-W. Su, Y.-H. Chang et al., “Skyline localization for mountain images,” Proc. of the IEEE Int. Conf. on Multimedia and Expo, pp. 1-6, San Jose, USA, July 15-19, 2013.
  7. [7] T. Ahmad, G. Bebis, M. Nicolescu et al., “An Edge-Less Approach to Horizon Line Detection,” Proc. of the IEEE Int. Conf. on Machine Learning and Applications, pp. 1095-1102, Miami, USA, December 9-11, 2015.
  8. [8] T. Ahmad, G. Bebis, E. E. Regentova et al., “A Machine Learning Approach to Horizon Line Detection Using Local Features,” Proc. of the 9th Int. Symp. on Advances in Visual Computing, pp. 181-193, Rethymnon, Greece, July 29-31, 2013.
  9. [9] A. P. Yazdanpanah, E. E. Regentova, V. Muthukumar et al., “Real-Time Horizon Line Detection Based on Fusion of Classification and Clustering,” Int. J. of Computer Applications, Vol.121, No.10, pp. 5-11, No.10, 2015.
  10. [10] L. Porzi, S. R. Buló. Lanz, P. Valigi, and E. Ricci, “Learning Contours for Automatic Annotations of Mountains Pictures on a Smartphone,” Proc. of the ACM/IEEE Int. Conf. on Distributed Smart Cameras, Article No.13, pp. 1-6, November 4-7, 2014.
  11. [11] R. Verbickas and A. Whitehead, “Sky and Ground Detection Using Convolutional Neural Networks,” Proc. of Int. Conf. on Machine Vision and Machine Learning, Paper No.64, pp. 64-1-64-10, Prague, Czech Republic, August 14-15, 2014.
  12. [12] A. P. Yazdanpanah, E. E. Regentova, A. K. Mandava et al., “Sky Segmentation by Fusing Clustering with Neural Networks,” Proc. of the 9th Int. Symp. on Visual Computing, Vol.8034, pp. 663-672, Rethymnon, Greece, July 29-31, 2013.
  13. [13] L. Porzi, S. R. Bulò, and E. Ricci, “A Deeply-Supervised Deconvolutional Network for Horizon Line Detection,” Proc. of the 24th ACM Int. Conf. on Multimedia, pp. 137-141, Amsterdam, The Netherlands, October 15-19, 2016.
  14. [14] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Proc. of Int. Conf. on Medical Image Computing and Computer-Assisted Intervention, pp. 234-241, Munich, Germany, October 5-9, 2015.
  15. [15] D. Frajberg, P. Fraternali, and R. N. Torres, “Convolutional Neural Network for Pixel-Wise Skyline Detection,” Proc. of the 26th Int. Conf. on Artificial Neural Networks, pp. 12-20, Alghero, Italy, September 11-14, 2017.
  16. [16] T. Ahmad, P. Campr, M. Čadik et al., “Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection,” Proc. of Int. Joint Conf. on Neural Networks, pp. 4436-4443, Anchorage, USA, May 14-19, 2017.
  17. [17] O. Saurer, G. Baatz, K. Köser et al., “Image Based Geo-Localization in the Alps,” Int. J. of Computer Vision, Vol.116, No.3, pp. 213-225, 2016.
  18. [18] J. Dai, L. Yi, K. He et al., “R-FCN: Object Detection via Region-Based Fully Convolutional Networks,” Proc. of the 30th Conf. on Neural Information Processing Systems, pp. 379-387, Barcelona, Spain, December 5-10, 2016.
  19. [19] V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.39, No.12, pp. 2481-2495, 2017.
  20. [20] K. He, X. Zhang, S. Q. Ren, and J. Sun, “Identity mappings in deep residual networks,” Proc. of the 14th European Conf. on Computer Vision, pp. 630-645, Amsterdam, The Netherlands, October 11-14, 2016.
  21. [21] K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” Proc. of the European Conf. on Computer Vision, pp. 346-361, Zurich, Switzerland, September 6-12, 2014.
  22. [22] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 3431-3440, Boston, USA, June 7-12, 2015.
  23. [23] Y. Liu, M.-M. Cheng, X. Hu et al., “Richer convolutional features for edge detection,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.41, No.8, pp. 1939-1946, 2019.
  24. [24] K. He, J. Sun, and X. O. Tang, “Guided image filtering,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.35, No.6, pp. 1397-1409, 2013.
  25. [25] D.-A. Huang, L.-W. Kang, Y.-C. F. Wang, and C.-W. Lin, “Self-learning based image decomposition with applications to single image denoising,” IEEE Trans. on Multimedia, Vol.16, No.1, pp. 83-93, 2014.
  26. [26] L.-W. Kang, C.-W. Lin, and Y.-H. Fu, “Automatic single-image-based rain streaks removal via image decomposition,” IEEE Trans. on Image Processing, Vol.21, No.4, pp. 1742-1755, 2012.
  27. [27] X. Fu, J. Huang, D. Zeng et al., “Removing rain from single images via a deep detail network,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1715-1723, Honolulu, USA, July 21-26, 2017.
  28. [28] G. Huang, Z. Liu, L. van der Maaten et al., “Densely connected convolutional networks,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2261-2269, Honolulu, USA, July 21-26, 2017.
  29. [29] S. Xie and Z. Tu, “Holistically-nested edge detection,” Proc. of the IEEE Int. Conf. on Computer Vision, pp. 1395-1403, Santiago, Chile, December 7-13, 2015.
  30. [30] J. Brejcha and M. Čadík, “GeoPose3K: mountain landscape database for camera pose estimation in outdoor environments,” Image and Vision Computing, Vol.66, pp. 1-14, 2017.
  31. [31] L.-C. Chen, G. Papandreou, F. Schroff et al., “Rethinking atrous convolution for semantic image segmentation,” arXiv preprint, arXiv:1706.05587, 2017.
  32. [32] H. Zhao, J. Shi, X. Qi et al., “Pyramid scene parsing network,” Proc. of the IEEE Conf. on computer vision and pattern recognition, pp. 6230-6239, Honolulu, USA, July 21-26, 2017.
  33. [33] K. He, X. Zhang, S. Ren et al., “Deep residual learning for image recognition,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 770-778, Las Vegas, USA, June 27-30, 2016.

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