The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
권호 표지

A New Snakes Algorithm Combined with Disparity Information in the Stereo Images

스테레오 영상에서 변이 정보를 결합한 새로운 스네이크 알고리즘

  • 김신형 (배재대학교 정보통신공학과 비디오연구실) ;
  • 전병태 (한국전자통신연구원) ;
  • 장종환 (배재대학교 정보통신공학과 비디오연구실)
  • Published : 2003.11.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose a method that improves the snakes algorithm well known as previously active contour model. Generally, the previous snakes algorithm applied to the 2-D images doesn't get the good results due to the influences about other objects adjacent to contour of object to be extracted. Users directly set the initial snakes points near to the contour of the object to get better results. In this paper, using the disparity information of the stereo images, a new algorithm of the object segmentation is proposed to reduce the influences adjacent to the contour of object. Users can establish initial snakes points automatically from the setting of the interested regions.

본 논문에서는 능동윤곽모델(active contour model)로 잘 알려져 있는 스네이크(snakes)알고리즘을 MPEG-4 기반의 스테레오 영상의 객체분할에 적용하는 방법을 제안한다. 일반적으로 2차원 영상에 적용하는 기존 스네이크 알고리즘은 객체의 윤곽이 아닌 주변의 영향으로 만족할 만한 결과를 얻지 못한다. 따라서 관심 객체의 윤곽선에 가까이 초기 스네이크 포인트를 사용자가 직접 설정해야 한다. 본 논문에서는 스테레오 영상의 변이(disparity)정보를 이용하여 객체의 윤곽선 주위의 영향을 줄여 객체분할의 성능을 개선하였고, 사용자가 영역설정을 통해 초기 스네이크 포인트를 자동으로 설정할 수 있게 하였다.

Keywords

References

  1. R. C. Gonzalez and R. E. Woods, DigitalImage Processing, Addision Wesley Publishing company, 1992
  2. R. M. Haralick and L. G. Shapiro, 'SURVEY: Image Segmentation Techniques,' Computer Vision Graphics and Image Processing, vol.29, 1985
  3. S. Beucher and C. Lantuejoul, 'Use ofWatershed in Contour Detection,' Proceedings of the International Workshopon Image Processing. CCETT/IRISA, Rennes,France, 1979
  4. L. Vincent and P. Soille, 'Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, pp. 583-598, 1991 https://doi.org/10.1109/34.87344
  5. ISO/IEC JTC/SC29/WG11/W4350 : 'Information Technology - Coding of Audio-Visual Objects Part2: visual' ISO/IEC 14496-2, July 2001
  6. R. Adams and L. Bischof, ' Seeded Region Growing,' IEEE Trans. Pattern Anal. Machine Intell. vol. 16, no. 6, pp. 641-647, June 1994 https://doi.org/10.1109/34.295913
  7. M. Kass, A. Witkin, and D. Terzopoulos,'Snake: Active Contour Models,' Int'l J.Computer Vision, vol. 1, no. 4, pp.321-331, 1987
  8. D. J. Williams and M. Shah, 'A Fast Aleorithm for Active Contours And Curvature Estimation,' Computer Vision, Graphics, and Image Processing, vol. 55, pp. 14-26, 1992
  9. K. M. Lam, and H. Yan, 'Fast Greedy Algorithm for Active Contours,' Electron Lett., vol. 30, pp. 21-23, 1994 https://doi.org/10.1049/el:19940040
  10. D.R. Clewer, L. J. Luo, C. N. Canagarajah,D.R. Bull, M. H. Barton,' Efficient Multiview Image Compression using Quadtree disparity estimation,' ISCAS 2001, vol. 5,pp. 295-298, May 2001
  11. Y. J. Song, 'Improved Disparity Estimation Algorithm with MPEG-2's Scalability for Stereoscopic Sequences,' IEEE, Trans.Consumer Elect., vol. 42, no. 3, pp.306-311, August 1996 https://doi.org/10.1109/30.536218
  12. T. Kanade and M. Okutoni, ' A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment,' IEEE Trans.Pattern Analysis and Machine Intelligence,vol. 16, no.9, pp 920-932, 1994 https://doi.org/10.1109/34.310690
  13. A. Redert, E. Hendriks and J. Biemond 'Correspondence Estimation in Image Pairs,'IEEE Signal Processing Mag., vol. 16, no.3, pp. 29-46, May 1999 https://doi.org/10.1109/79.768571