대한전자공학회논문지SP (Journal of the Institute of Electronics Engineers of Korea SP)

대한전자공학회 (The Institute of Electronics and Information Engineers)
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산악 영상에서의 지평선 검출 알고리즘

Robust Skyline Extraction Algorithm For Mountainous Images

  • 투고 : 2010.04.09
  • 심사 : 2010.06.09
  • 발행 : 2010.07.25
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초록

무인 로봇이나 무인 항공기 등의 위치 추정 등에 사용되는 산악 영상에서 지평선을 검출하는 것은 지평선의 복잡성, 환경에 의한 가려짐, 영상의 노이즈 때문에 매우 힘들다. 이러한 어려움에도 불구하고 지평선 검출은 무인 이동체에 다양하게 적용될 수 있는 매우 중요한 연구 주제이다. 본 논문에서는 다중 스케일 케니 영상과, 위상 정보, 그리고 영상 속에서의 지평선의 위치 정보를 이용하여 지평선 검출 알고리즘을 개발 하였다. 다중 스케일 케니 영상은 추정(localization)에 강한 고 스케일 케니 영상과 탐색(detection)에 강한 저 스케일 케니 영상으로 구성된다. 알고리즘의 적절한 단계에 각각의 케니 영상을 선택적으로 적용함으로 복잡한 환경에서도 좋은 지평선 검출 결과를 얻을 수 있다. 제안된 알고리즘의 성능은 다양한 영상을 통해 검증되었으며 기존의 기법과 비교되었다.

Skyline extraction in mountainous images which has been used for navigation of vehicles or micro unmanned air vehicles is very hard to implement because of the complexity of skyline shapes, occlusions by environments, dfficulties to detect precise edges and noises in an image. In spite of these difficulties, skyline extraction is avery important theme that can be applied to the various fields of unmanned vehicles applications. In this paper, we developed a robust skyline extraction algorithm using two-scale canny edge images, topological information and location of the skyline in an image. Two-scale canny edge images are composed of High Scale Canny edge image that satisfies good localization criterion and Low Scale Canny edge image that satisfies good detection criterion. By applying each image to the proper steps of the algorithm, we could obtain good performance to extract skyline in images under complex environments. The performance of the proposed algorithm is proved by experimental results using various images and compared with an existing method.

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참고문헌

  1. Ettinger, Scott M., Nechyba, Michael C., Ifju, Peter G. and Waszak, Martin, "Vision-guide flight stability and control for micro air vehicles," IROS '02, proc. IEEE/RSJ International Conference on Intelligent Robots and System, pp.2134-2140, 2002.
  2. Messi, Mark, et al., "Vision chip flight stability and control for micro air vehicles," ISCAS '03, proc. IEEE International Conference on Circuit System, pp.786-789, 2003.
  3. Truchetel, F., et al., "Attitude Measurement by Artificial Vision," Measurement Science and Technology, vol. 17, pp.101-110, 2006. https://doi.org/10.1088/0957-0233/17/1/017
  4. Stewart, J.A., "Fast horizon computation at all points of a terrain with visibility and shading applications," IEEE Trans. Visual. Comput.Graphics, 4(1), pp.82-93, 1998. https://doi.org/10.1109/2945.675656
  5. Fang, M., Chiu, M.-Y., Liang, C.-C. and Singh, A., "Skyline for video-based virtual rail for vehicle navigation," Proc. IEEE International Sympos. On Intelligent Vehicles, pp.207-212, 1993.
  6. Stein, F. and Medioni, G., "Map-based localization using the panoramic horizon," IEEE Trans. On Robotics and Automation, 11(6), pp. 892-896, 1995. https://doi.org/10.1109/70.478436
  7. Cozman, F., Krotkov, E., "Automatic mountain detection and pose estimation for teleoperation oflunar rovers," Proc. Of the International Conference on Robotics and Automation, pp.2452-2457, 1997.
  8. Talluri, R. and Aggarwal, J., "Position estimation for an autonomous mobile robot in an outdoor environment," IEEE Trans. Robotics and Automation, 8(5), pp.573-584, 1992. https://doi.org/10.1109/70.163782
  9. Lie, W.N., Lin, T.C.-I., Lin, T.-C. and Hung, K.-S., "SA robust dynamic programming algorithm to extract skyline in images for navigation," Pattern Recognition Letters, 26, pp.221-230, 2005. https://doi.org/10.1016/j.patrec.2004.08.021
  10. Woo, J.H., Kweon, I.S., "Robust horizon and peak extraction for vision-based navigation," MVA'06 Proc. IAPR workshop on Machine Vision Applications, 2005.
  11. Canny, J., "A computational theory for edge detection," IEEE Trans. On Pattern Recognition and Machine Intelligence, vol.26, no.6, pp.679-698, 1986.
  12. Trucco, E. and Verri, A., "Introductory techniques for 3-d computer vision," Prentice Hall, Upper Saddle River, NJ, 1998.
  13. Forsyth, D. A. and Ponce, J., "Computer Vision A Modern Approach," Prentice Hall, Upper Saddle River, NJ, 2003.