윤곽선 정보를 이용한 동영상에서의 객체 추출

Video Object Extraction Using Contour Information

  • 김재광 (한국과학기술원 전기 및 전자공학과) ;
  • 이재호 (한국과학기술원 전기 및 전자공학과) ;
  • 김창익 (한국과학기술원 전기 및 전자공학과)
  • Kim, Jae-Kwang (School of Electrical Engineering, Korea Advanced Institute of Science and Technology) ;
  • Lee, Jae-Ho (School of Electrical Engineering, Korea Advanced Institute of Science and Technology) ;
  • Kim, Chang-Ick (School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
  • 투고 : 2010.07.16
  • 심사 : 2010.09.30
  • 발행 : 2011.01.25

초록

본 논문에서는 객체의 윤곽선 정보에 기반한 수정된 그래프컷(Graph-cut) 알고리즘을 이용하여 동영상에서 효율적으로 객체를 추출하는 방법을 제안한다. 이를 위해 먼저, 첫 프레임에서 자동 추출 알고리즘 이용하거나 사용자와의 상호작용을 통해 영상에서 객체를 분리한다. 객체의 형태 정보를 상속시키기 위해 이전 프fp임에서 추출된 객체 윤곽선의 움직임을 예측한다. 예측된 윤곽선을 기준으로 블록 단위 히스토그램 역투영(Block-based Histogram Back-projection) 알고리즘을 수행하여 다음 프레임의 각 픽셀에 대한 객체와 배경의 컬러 모델을 형성한다. 또한 윤곽선을 중심으로 전체 영상에 대한 로그함수 기반의 거리 변환 지도(Distance Transform Map)를 생성하고 인접 픽셀간의 연결(link)의 확률을 결정한다. 생성된 컬러 모델과 거리 변환 지도를 이용하여 그래프를 형성하고 에너지를 정의하며 이를 최소화하는 과정을 통해 객체를 추출한다. 다양한 영상들에 대한 실험 결과를 통해서 기존의 객체 추출 방법보다 제안하는 방법이 객체를 보다 정확하게 추출함을 확인할 수 있다.

In this paper, we present a method for extracting video objects efficiently by using the modified graph cut algorithm based on contour information. First, we extract objects at the first frame by an automatic object extraction algorithm or the user interaction. To estimate the objects' contours at the current frame, motion information of objects' contour in the previous frame is analyzed. Block-based histogram back-projection is conducted along the estimated contour point. Each color model of objects and background can be generated from back-projection images. The probabilities of links between neighboring pixels are decided by the logarithmic based distance transform map obtained from the estimated contour image. Energy of the graph is defined by predefined color models and logarithmic distance transform map. Finally, the object is extracted by minimizing the energy. Experimental results of various test images show that our algorithm works more accurately than other methods.

키워드

과제정보

연구 과제 주관 기관 : 정보통신산업진흥원

참고문헌

  1. G. Hayit, G. Jacob, and M. Arnoldo, "A Probabilistic Framework for Spatio-Temporal Video Representation & Indexing," 7th European Conference on Computer Vision-Part IV, vol. 2353, pp. 461-475, 2002.
  2. L. Lijie and F. Guoliang, "Combined key-frame extraction and object-based video segmentation," IEEE Transactions on Circuits and Systems for Video Technology, vol. 15, pp. 869-884, 2005. https://doi.org/10.1109/TCSVT.2005.848347
  3. O. Javed, Z. Rasheed, K. Shafique, Mubarak Shah, "Tracking Across Multiple Cameras With Disjoint Views," IEEE International Conference on Computer Vision, vol. 2, pp. 952-957, 2003.
  4. D. Comaniciu, V. Ramesh, P. Meer, "Kernel-Based Object Tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564-577, 2003. https://doi.org/10.1109/TPAMI.2003.1195991
  5. J. Kang, I. Cohen, and G. Mediono, "Object reacquisition using geometric invariant appearance model," IEEE International Conference on Pattern Recongnition, pp. 759-762, 2004.
  6. A. Yilmax, X. Li, and M. Shah, "Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 1531-1536, 2004. https://doi.org/10.1109/TPAMI.2004.96
  7. Y. Li, J. Sun, and H.Y. Shum, "Video Object Cut and Paste," ACM Transactions on Graphics, vol. 24, no. 3, pp. 595-600, 2005. https://doi.org/10.1145/1073204.1073234
  8. B. Li, B. Yuan, and Y. Sun, "Moving Object Segmentation Using Dynamic 3D Graph Cuts and GMM," IEEE International Conference on Signal Processing, vol 2, pp. 16-20, 2006.
  9. S. Sun, D.R. Haynor, and Y. Kim, "Semiautomatic Video Object Segmentation Using VSnakes," IEEE Transactions on Circuit and System for Video Technology, vol. 13, no. 1, pp. 75-82, 2003. https://doi.org/10.1109/TCSVT.2002.808089
  10. S. Yonggang and W. C. Karl, "A Real-Time Algorithm for the Approximation of Level-Set-Based Curve Evolution," IEEE Transactions on Image Processing, vol. 17, pp. 645-656, 2008. https://doi.org/10.1109/TIP.2008.920737
  11. P. Harper and R. B. Reilly, "Color based video segmentation using level sets," IEEE International Conference on Image Processing, vol. 3, pp. 480-483, 2000.
  12. C. Jung, B. Kim, and C. Kim, "Automatic Segmentation of Salient Objects Using Iterative Reversible Graph Cut," will be appeared to IEEE International Conference on Multimedia & Expo, 2010.
  13. Y. Y. Boykov and M. P. Jolly, "Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images," IEEE International Conference on Computer Vision, vol. 1, pp. 105-112, 2001.
  14. C. Rother, V. Kolmogorov, and A. Blake, "GrabCut: Interactive Foreground Extraction using Iterated Graph Cuts," ACM Transactions on Graphics, vol. 23, no. 3, pp. 309-314, 2004. https://doi.org/10.1145/1015706.1015720
  15. X. Hou and L. Zhang, "Saliency detection: A Spectral Residual Approach," IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1–8, 2007.
  16. J.F. Talbot, X. Xu, "Implementing GrabCut," Brigham Young University, 2006.
  17. Z. Liu, J. Cu, L. Shen, Z. Zhang, "Efficient Video Object Segmentation Based on Gaussian Mixture Model and Markov Random Field," IEEE International Conference on Signal Processing, pp. 1006-1009, 2008.
  18. J. Lee, W. Lee, D. Jeong, "Object Tracking Method Using Back-Projection of Multiple Color Histogram Models," IEEE International Symposium on Circuits and Systems, vol. 2, pp. 668-671, 2003.
  19. G. Borgefors, "Distance transformations in digital images," Computer Vision, Graphics, and Image Processing, vol. 34, pp. 344-371, 1986. https://doi.org/10.1016/S0734-189X(86)80047-0
  20. F. Y. C. Shih and O. R. Mitchell, "A mathematical morphology approach to Euclidean distance transformation," IEEE Transactions on Image Processing, vol. 1, pp. 197-204, 1992.
  21. Z. Garrett and H. Saito, "Live Video Object Tracking and Segmentation Using Graph Cuts," IEEE International Conference on Image Processing, pp. 1576-1579, 2008.