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Efficient Horizon Line Detection Using an Energy Function

Published: 20 September 2017 Publication History

Abstract

In this work we propose a new method for horizon line detection in marine environment images captured by either visible light or infrared cameras. A common method for horizon line detection is based on edge detection and Hough transform. This method has serious drawbacks when the horizon is not a clear straight line or when the image contains other straight lines.
Our method replaces the Hough transform with a more sophisticated method based on an image operator called seam. A seam is an optimal 8-connected path of pixels in a single image going from left to right or vice versa. An energy function is used and the seam optimality is defined by the steepest energy descent. We compare the accuracy and relative speed of our method to existing methods for a group of test images. These images are real-life photographs at different spatial resolutions, levels of blurriness, and varying contrast and brightness conditions. Our experiments show that our method increases the accuracy of horizon detection compared to other similar techniques.

References

[1]
Gershikov, E., Libe, Tz. and Kosolapov, S. 2013. Horizon Line Detection in Marine Images: Which Method to Choose?, International Journal On Advances in Intelligent Systems, 6, 1 and 2 (June 2013), 79--88.
[2]
Fefilatyev, S., Smarodzinava, V., Hall, L.O. and Goldgof, D.B. 2006. "Horizon detection using machine learning techniques". Proc. International Conerence on Machine Learning and Applications (2006), 17--21.
[3]
Lifshitz, 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 (IJCER), 3, 3 (March 2013), 226--233.
[4]
Duda, R. O. and Hart, P. E. 1972. Use of the Hough Transformation to Detect Lines and Curves in Pictures, Communications of the ACM, 15, 1 (1972), 11--15.
[5]
Trahanias, P.E. and Venetsanopoulos, A.N. 1993. Color Edge Detection Using Vector Order Statistics, IEEE Transactions on Image Processing, 2, 2 (April 1993), 135--143.
[6]
Mittal, A., Sofat, S. and Hancock E. 2012. Detection of edges in Color Images: A review and evaluative comparison of
[7]
state-of-the-art techniques, Autonomous and Intelligent Systems Lecture Notes in Computer Science (2012), 250--259.
[8]
Gershikov, E. 2014, Is Color Important for Horizon Line Detection?, Proceedings of Advanced Technologies for Communications (ATC2014) (Hanoi, Vietnam, Oct. 15--17, 2014).
[9]
Canny, J. 1986. A Computational Approach To Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 6 (1986), 679--698.
[10]
Koschan, A. and Abidi, M. 2005. Detection and classification of edges in color images: A review of vector valued techniques, IEEE Signal Processing Magazine, 22 (Jan. 2005), 64--73.
[11]
Chen, X. and Chen, H. 2010. A novel color edge detection algorithm in RGB color space, Proceedings of IEEE 10th International Conference on Signal Processing (Beijing, China, Oct. 2010), 793--796.
[12]
Avidan, S. and Shamir, A. 2007. Seam Carving for Content-Aware Image Resizing, Proceedings of ACM SIGGRAPH (New York, USA, 2007).

Cited By

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  • (2022)SA‐BiSeNet: Swap attention bilateral segmentation network for real‐time inland waterways segmentationIET Image Processing10.1049/ipr2.1262517:1(166-177)Online publication date: 6-Sep-2022
  • (2020)Adaptive Semantic Segmentation for Unmanned Surface Vehicle NavigationElectronics10.3390/electronics90202139:2(213)Online publication date: 24-Jan-2020
  • (2020)A Survey on Edge Detection based recent Marine Horizon Line Detection Methods and their Applications2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)10.1109/iCoMET48670.2020.9073895(1-5)Online publication date: Jan-2020
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  1. Efficient Horizon Line Detection Using an Energy Function

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    cover image ACM Conferences
    RACS '17: Proceedings of the International Conference on Research in Adaptive and Convergent Systems
    September 2017
    324 pages
    ISBN:9781450350273
    DOI:10.1145/3129676
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 20 September 2017

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    Author Tags

    1. Horizon detection
    2. edge detection
    3. energy function
    4. image analysis
    5. marine images
    6. seam

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    RACS '17 Paper Acceptance Rate 48 of 207 submissions, 23%;
    Overall Acceptance Rate 393 of 1,581 submissions, 25%

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    Cited By

    View all
    • (2022)SA‐BiSeNet: Swap attention bilateral segmentation network for real‐time inland waterways segmentationIET Image Processing10.1049/ipr2.1262517:1(166-177)Online publication date: 6-Sep-2022
    • (2020)Adaptive Semantic Segmentation for Unmanned Surface Vehicle NavigationElectronics10.3390/electronics90202139:2(213)Online publication date: 24-Jan-2020
    • (2020)A Survey on Edge Detection based recent Marine Horizon Line Detection Methods and their Applications2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)10.1109/iCoMET48670.2020.9073895(1-5)Online publication date: Jan-2020
    • (2019)Autonomous Visual Perception for Unmanned Surface Vehicle Navigation in an Unknown EnvironmentSensors10.3390/s1910221619:10(2216)Online publication date: 14-May-2019

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