The Grid Pattern Segmentation Using Hybrid Method

하이브리드 방법을 이용한 격자 패턴의 세그먼테이션

  • Published : 2004.02.01

Abstract

This paper presents an image segmentation algorithm to obtain the 3D body shape data that the grid pattern and the body contour lute in the background image are extracted using the new proposed hybrid method. The body contour line is extracted based on maximum biased anisotropic recognition(MaxBAR) algorithm which recognizes the most strong and robust edges in the image since the normal derivative at the edges is large, while the tangential derivatives can be small. The grid patterns within body contour lines are extracted by grid pattern detection (GPD). The body contour lilies and the grid patterns are combined. The consecutive run test based on heuristic method is used to link the disconnected line and reduce noise line. This proposed segmentation method is more effective than the conventional method which uses a gradient and a laplacian operator, verified with application two conventional method.

본 논문은 하이브리드 방법을 사용하여 영상내의 체형 외곽 선과 격자 패턴을 추출하여 3차원 체형 데이터를 획득하기 위한 새로운 영상분할 알고리즘을 제안한다. 체형 외곽 선을 추출하기 위한 영상분할 방법으로 최대 값 인식 알고리즘을 사용하였다. 이 방법은 에지에서의 접선 방향 값은 작지만 법선 방향 값은 큰 성질을 이용하여 일정 영역내의 픽셀들간의 변화 값 중 최대 값을 인식하는 알고리즘이다. 그리고 체형 외곽내의 격자 패턴은 격자 패턴 검출 알고리즘을 사용하여 추출하였다. 추출된 체형 외곽 선과 격자 패턴을 결합한 후 휴리스틱 방법인 연속 길이 테스트에 치한 격자 패턴의 연결 및 잡음제거를 하였다. 본 논문에서 제안한 영상분할 방법은 기존의 기울기나 라플라시안 연산방법보다 매우 효과적인 결과를 가져 왔다.

Keywords

References

  1. D. Geiger, A. Gupta, L. A. Costa, and J. Vlontzos, 'Dynamic programming for detecting, tracking, and matching deformable contours', IEEE Trans. PAMI, Vol. 17, No. 3, pp. 294-302, 1995 https://doi.org/10.1109/34.368194
  2. Hyo-Kyung Sung, Sung-Gan Kim, and Heung-Moon Choi, 'Detection and Segm-entation of Circular Shaped Objects Using Spatial Information on Boundary Neighborhood', 전자공학회논문지-S 제 34권, 제 6호, 6. 1997
  3. In Gab Jeong, Yong Suk Kim, Ki Ho Hyun, Eung Joo Lee, and Yeong Ho Ha, 'Range Image Segmentation and Classification Using Cooperative Relaxational Algorithm Between H-K Curvatures', 전자공학회논문지-S 제 34권 제 8호, 1997
  4. Boo-Hyoung Lee and Hem-Soo Hahn, Surface Segmentation and Feature Description using the Signature Technique, 전자공학회 논문지-S, 제34권 제12호, 1997
  5. L. Alvarez, P.Lions, and M.Morel, 'Image selective smoothing and edge detection by nonlinear diffusion, II', SIAM J. Numer. AnaL, 29(1992), pp. 845-866 https://doi.org/10.1137/0729052
  6. G. Aubert and P. Komprobst, 'Mathmatical Problems in Image Processing', No. 147 in Applied Mathematics Sciences, SpringerVerlag, New York, 2002
  7. A. Marquina and S. Osher, 'Explicit Algorithms for a new time dependent model based on level set motion for nonlinear deblurring and noise removal', SIAM J. Sci. Comput., 22(2000), pp387-405 https://doi.org/10.1137/S1064827599351751
  8. T.F. Chan and LA Vese.,'Image segmentation using level sets and the piecewise constant Mumford-Shah model', Technical report, UCLA Dept. of Math., CAM 00-14, 2000
  9. S. Osher and R Fedkiw,'Level set methods: An overview and some recent results', J. Comput. Phys., 169(2):463-502, May 2001 https://doi.org/10.1006/jcph.2000.6636
  10. D. Peng, B. Merriman, S.. Osher, H.K. Zhao, and M. Kang, 'A PDE_based fast local level set method', J.Comput. Phys., 155(2):410 - 438, 1999 https://doi.org/10.1006/jcph.1999.6345
  11. J. C. Russ, 'The Image Processing Handbook', 2nd Ed., CRC Press, Fla., 1995
  12. R C. Gonzalez and R. E. Woods, 'Digital Image Processing, Addison-Wesley Publishing Company', Mass., 1993
  13. Bouman, C., and Liu, B. 'Multiple Resolution Segmentation of Textured Images', IEEE Trans. Pattern Anal. Machine Intell.,'Vol. 13, No.2, pp. 99-113, 1991 https://doi.org/10.1109/34.67641
  14. Jang, B.K., and Chin, RT. 'Analysis of Thinning Algorithms Using Mathematical Morphology . IEEE Trans., Pattern Anal. Machine Intell., Vol. 12, No.6, pp. 541-551, 1990 https://doi.org/10.1109/34.56190