Journal of Internet Computing and Services (인터넷정보학회논문지)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Color Object Segmentation using Distance Regularized Level Set

거리정규화 레벨셋을 이용한 칼라객체분할

  • 란 안 (전남대학교 전자컴퓨터공학과) ;
  • 이귀상 (전남대학교 전자컴퓨터공학과)
  • Received : 2012.04.25
  • Accepted : 2012.07.26
  • Published : 2012.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

Object segmentation is a demanding research area and not a trivial problem of image processing and computer vision. Tremendous segmentation algorithms were addressed on gray-scale (or biomedical) images that rely on numerous image features as well as their strategies. These works in practice cannot apply to natural color images because of their negative effects to color values due to the use of gray-scale gradient information. In this paper, we proposed a new approach for color object segmentation by modifying a geometric active contour model named distance regularized level set evolution (DRLSE). Its speed function will be designed to exploit as much as possible color gradient information of images. Finally, we provide experiments to show performance of our method with respect to its accuracy and time efficiency using various color images.

객체분할은 영상처리와 컴퓨터비전분야의 상당히 어려운 연구대상이다. 그레이스케일 영상에 대한 영상분할은 매우 많은 방법이 발표되었으며 다양한 영상특징과 처리방법이 제시되었다. 이러한 방법들은 대개 자연상태의 칼라 영상에 적용되기 어렵다. 본 논문에서는 기하학적인 Active Contour 모델의 수정된 형태, 즉 거리정규화레벨셋(distance regularized level set evolution: DRLSE)을 이용한 방법을 제시하여 스피드 함수가 이러한 칼라요소를 반영하도록 하였으며 실험결과 정확성과 시간효율성에 있어서 우수한 결과를 보여주었다.

Keywords

References

  1. Chunming Li, Chenyang Xu, Changfeng Gui, and Martin D. Fox, "Level set evolution without re-initialization: A new variational formulation," Proceedings of the 2005 IEEE Computer Society Conference on Conputer Vision and Pattern Recognition, CVPR, Vol. 1, 2005, pp. 430-436.
  2. Chunming Li, Chenyang Xu, Changfeng Gui, and Martin D. Fox, "Distance regularized level set evolution," IEEE Transactions on Image Processing, Vol. 19, No. 12, Dec. 2010, pp. 3242-3254.
  3. M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: active contour models," International Journal of Computer Vision, Vol. 1, 1987, pp. 321-331.
  4. S. Osher and J. Sethian, "Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations," Journal of Computational Physics, Vol. 79, No. 1, Nov. 1988, pp. 12-49. https://doi.org/10.1016/0021-9991(88)90002-2
  5. V. Caselles, R. Kimmel, and G. Sapiro, "Geodesic active contours," in International Journal of Computer Vision, 1997, pp. 61-79.
  6. Chenyang Xu and Jerry L. Prince, "Gradient vector flow: A new external force for snakes," in IEEEProc. Conference on Computer Vision and Pattern Recognition, CVPR, 1997, pp. 66-71.
  7. S. Osher and R. Fedkiw, "Level set methods and dynamic implicit surfaces," in Springer, New York, 2003.
  8. J. A. Sethian, "Level set methods and Fast marching methods," in Cambridge University Press, Cambridge, 1999.
  9. Tony F. Chan and Luminita A. Vese, "Active contours without edges," in IEEE Transactions on Image Processing, Vol. 10, No. 2, Feb. 2001, pp. 266-277. https://doi.org/10.1109/83.902291
  10. Chunming Li, Chenyang Xu, Kishori M. Konwar, and Martin D. Fox, "Fast distance preserving level set evolution for Medical image segmentation," IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV, 2006, pp. 1-7.
  11. H.J. Wang, M. Liu, and W.L. Ma, "Color Image Segmentation Based on a New Geometric Active Contour Model," IEEE International Conference on Machine Vision and Human-machine Interface, Apr. 2010, pp. 6-9.
  12. T. Brox, A. Bruhn, and J. Weickert, "Variational motion segmentation with level sets," in Computer Vision, ECCV 2006, pp. 471-483, Springer.
  13. http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html
  14. http://www.engr.uconn.edu/-cmli/DRLSE/

Cited by

  1. A New Hyper Parameter of Hounsfield Unit Range in Liver Segmentation vol.21, pp.3, 2012, https://doi.org/10.7472/jksii.2020.21.3.103