DOI QR코드

DOI QR Code

Balloon-like Active Contour Model Using Variable Closet Points

가변적인 폐쇄 점들을 이용한 풍선 형태의 능동 윤곽 모델

  • Yi, Chu-Ho (Dept. of Electronics and Computer Engin., Hanyang University) ;
  • Jeong, Seung-Do (Dept. of Information and Communication Engin., Hanyang Cyber University) ;
  • Cho, Jung-Won (Dept. of Computer Education, Jeju National University)
  • 이주호 (한양대학교 전자컴퓨터통신공학과) ;
  • 정승도 (한양사이버대학교 정보통신공학과) ;
  • 조정원 (제주대학교 컴퓨터교육과)
  • Received : 2012.05.31
  • Accepted : 2012.08.09
  • Published : 2012.08.31

Abstract

Active contour model or snake is widely used for segmentation method in the area of the image processing and computer vision. The main problem in the active contour model is that results are very dependent to the closet points of the numbers and the location in initial step. Especially, in case of balloon-like active contour model, the small region which consist of intial closet points are expanded until the edge is reached. It is a serious problem because the considered region are huge with limited points. To solve this problem, in this paper, we propose the method that the number of closet points could be change based on the distance between points.

능동 윤곽 모델 혹은 스네이크는 영상처리 및 컴퓨터 비전 분야에서 널리 사용되는 분할 방법이다. 능동 윤곽 모델의 가장 큰 문제점은 초기에 설정하는 폐쇄 점들의 개수와 위치에 매우 민감하기 때문에 초기 설정에 따라 결과가 극명하게 달라진다는 것이다. 특히 풍선 형태의 능동 윤곽 모델의 경우 작은 크기에서 시작하여 윤곽선을 만날 때까지 발산하여 그 영역을 넓혀 가기 때문에 초기의 폐쇄 점들의 영역보다 훨씬 커질 경우 더욱 큰 문제점을 보일 수 있다. 본 논문에서는 이러한 문제를 해결하기 위해 폐쇄 점들 간의 거리에 따라 폐쇄 점들의 개수를 변화시킬 수 있는 새로운 알고리즘을 제안한다.

Keywords

References

  1. L. Shapiro and G. Stockman, Computer Vision, Prentice-Hall, 2001.
  2. M. Kass, A. Witkin, and D. Terzopoulos, "Snake: Active Contour Models," International Journal Computer Vision, pp. 321-331, 1988.
  3. L. Cohen and I. Cohen, "Finite-element Methods for Active Contour Models and Balloons for 2-D and 3-D Images," IEEE Trans. on Pattern Recognition and Machine Intelligence, vol.15, no.11, pp. 1131-1147, 1993. https://doi.org/10.1109/34.244675
  4. H. Wu, J. Liu, and C. Chui, "A Wavelet-Frame Based Image Force Model for Active Contouring Algorithm," IEEE Trans. on Image Processing, vol.9, no.11, pp. 1983-1988, 2000. https://doi.org/10.1109/83.877221
  5. A. Amini, S. Teharani, and T. Weymouth, "Using Dynamic Programming for Minimizing the Energy of Active Contours in Toe Presence of Hard Constraints," Proceeding of Second International Conference on Computer Vision, pp. 95-99, 1988.
  6. D. Williams and M. Shan, "A Fast Algorithm for Active Contours and Curvature Estimation," Proceeding of Conference on Computer Vision, pp. 592-595, 1990.
  7. C. Xu and J. Prince, "Snakes, Shapes, and Gradient Vector Flow," IEEE Trans. on Image Processing, vol.7, no.3, pp. 359-368, 1998. https://doi.org/10.1109/83.661186
  8. L. Cohen, "On Active Contour Models and Balloons," CVGIP: Image Understanding, pp. 1-18, 1991.
  9. Nho-Kyung Park, Sang-Bong Park, Min-Hyeong Park, "The Implementation of Motion Vector Detection Algorithm for the Optical-Sensor", Journal of The Institute of Webcasting, Internet and Telecommunication, VOL.10 No.5, pp. 251-257, October 2010.
  10. Su-Hyun Kim, Sang-Il Choi, Sung-Han Bae, Young-Dae Lee, Gu-Min Jeong, "Pattern Recognition using Feature Feedback : Performance Evaluation for Feature Mask", Journal of The Institute of Webcasting, Internet and Telecommunication, VOL.10 No.5, pp. 179-185, October 2010.
  11. Young-Sub Kim, Jong-Young Ahn, Sang-Bum Kim, Kang-In Hur, "Astudy on Robust Feature Imagefor Text ure ClassificationandDetection", Journal of The Institute of Webcasting, Internet and Telecommunication, VOL.10 No.5, pp. 133-138, October 2010.