AN ALGORITHM FOR SEA-SKY LINE DETECTION UNDER VISIBLE SEA IMAGE

Main Article Content

Oleksandr Shmatko
https://orcid.org/0000-0002-2426-900X
Volodymyr Aleksiyev
https://orcid.org/0000-0001-6767-7524
Liang Dong

Abstract

Subject of research is the process of sea-sky line detection, based on color feature. The purpose of this work is to develop the method is based on color feature as well as textural information. It consists of sea sky region extraction and horizon detection, which is more precise and fast no matter in the sceneries created by camera mounted on board or on shore. The tasks to be solved is: to propose the new linear fitting metric in sea-sky line extraction. The following results were obtained. The proposed linear fitting method was studies. The performance of proposed horizon detection method is estimated by comparison to other 3 state-of-art methods based on 13 challenging testing videos under different circumstances. The 3 methods are: the method based on discriminates and eigenvalues of covariance matrices in RGB space (H-DE); the method adopting probability distribution functions of sea and sky region (H-PDF) and the method by multi-scale cross modal linear feature (MSCM). The video sequences can be classified into two categories: the camera mounted on board and with camera mounted on shore horizon. Conclusion. The proposed linear fitting method can rectify the outlier values. The experimental results on a sequence of test videos demonstrate that the proposed sea-sky line detection method has a higher accuracy and it is more robust and efficient than other existed methods.

Article Details

How to Cite
Shmatko, O., Aleksiyev, V., & Dong, L. (2018). AN ALGORITHM FOR SEA-SKY LINE DETECTION UNDER VISIBLE SEA IMAGE. Advanced Information Systems, 2(4), 128–135. https://doi.org/10.20998/2522-9052.2018.4.22
Section
Applied problems of information systems operation
Author Biographies

Oleksandr Shmatko, National Technical University "Kharkiv Polytechnic Institute", Kharkiv

Candidate of Technical Sciences, Senior Lecturer

Volodymyr Aleksiyev, Simon Kuznets Kharkiv National University of Economics, Kharkiv

Doctor of Technical Sciences, Professor, Professor of the Department of Cybersecurity and Information Technology

Liang Dong, National Technical University «Kharkiv Polytechnic Institute», Kharkiv

Master Student

References

You, J.Y. & Chien, S.I. (2008), “Saturation enhancement of blue sky for increasing preference of scenery images”, IEEE Transactions on Consumer Electronics, Vol. 54, No. 2, pp. 762-768, DOI: https://doi.org/10. 1109/TCE.2008.4560158.

Ettinger, S.M., Nechyba, M.C., Ifju, P.G. and Waszak, M. (2004), “Vision-Guided Flight Stability and Control for Micro Air Vehicles”, IEEE/RSJ International Conference on Intelligent Robots and Systems, DOI:

https://doi.org/10.1109/IRDS.2002.1041582.

Lipschutz, I., Gershikov, E. and Milgrom, B. (2013), “New Methods for Horizon Line Detection in Infrared and Visible Sea Images”, International Journal Of Computational Engineering Research, Vol. 3, Issue. 3, pp. 226-233.

Shen, Y.F., Krusienski, D., Li, J. and Rahman, Z. (2013), “A Hierarchical Horizon Detection Algorithm”, IEEE GEOSCI REMOTE S,

(1), pp. 111-114.

Cornall, T.D., Egan, G.K. (2006), “Aircraft Attitude Estimation from Horizon Video”, Electron LETT, 42 (12), pp. 744-745.

Zhang, H., Yin, P., Zhang, X.O. and Shen, X.R. (2011), “A Robust Adaptive Horizon Recognizing Algorithm Based on Projection”, T I MEAS CONTROL, 33(6), pp. 734-751.

Luom J. and Etzm S. (2002), “A Physics-Motivated Approach to Detecting Sky”, Photographs. International Conference on Pattern Recognition, pp. 155-158.

Singhal, A. and Luo, J.B. (2003), “Hybrid Approach to Classifying Sky Regions in Natural Images”, Image and Video Communication and Processing, pp. 562-572.

Prasad, D.K., Rajan, D, Prasath, C.K., Rachmawati, L., Rajabally, E. and Quekm C, (2016), “MSCM-LiFe: Multi-scale Cross Modal Linear Feature for Horizon Detection”, Maritime Images. TENCON, pp. 1366-1370.

Otsu, N. (1979), “A Threshold Selection Method from Gray-Level Histograms”, IEEE Transactions on Systems, Man, and Cybernetics, 9(1), pp. 62-66, DOI: https://doi.org/10.1109/TSMC.1979.4310076.