Sea-level line extraction based on piecewise line detection

Bo Lei; Tingting Ren; Yingbin Liu; Jianwei Yu

doi:10.1117/12.2074412

24 November 2014 Sea-level line extraction based on piecewise line detection

Bo Lei, Tingting Ren, Yingbin Liu, Jianwei Yu

Proceedings Volume 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition; 93013L (2014) https://doi.org/10.1117/12.2074412
Event: International Symposium on Optoelectronic Technology and Application 2014, 2014, Beijing, China

Abstract

In infrared image, sea-level line could be hard to distinguish because of noises caused by wave clutters and sunlight conditions.This paper proposed a fast sea-level line extraction method which could localize the sea-level line in complex infrared sea-sky scenes. First, a down sample operation was performed to obtain a low resolution image which could reduce the time consumption without blurring the sea-level line, and then the Canny edge detection was carried out to extract edges in the scene. Second, the intersecting edges were separated by removing the joints of edges according to a certain rule, and the bounding rectangle of every short edge was obtained which helped to select straight lines, and then a long edge segmentation operation was used to count in possible sea-level line. Third, a line concatenation method was performed by their slopes and intercepts comparison. Finally, for sea-level line verification, the second-order vertical grads are calculated in the two sides of possible sea-level line. Experiments show that the proposed method is fast and effective for various kinds of infrared sea-sky scenes, and it is feasible even for the scenes where the sea-level line is blurring and hard to distinguish.

Citation Download Citation

Bo Lei, Tingting Ren, Yingbin Liu, and Jianwei Yu "Sea-level line extraction based on piecewise line detection", Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93013L (24 November 2014); https://doi.org/10.1117/12.2074412

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