Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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A Method for the Increasing Efficiency of the Watershed Based Image Segmentation using Haar Wavelet Transform

Haar 웨이블릿 변환을 사용한 Watershed 기반 영상 분할의 효율성 증대를 위한 기법

  • 김종배 (경북대학교 컴퓨터공학과) ;
  • 김항준 (경북대학교 컴퓨터공학과)
  • Published : 2003.03.01
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Abstract

This paper presents an efficient method for image segmentation based on a multiresolution application of a wavelet transform and watershed segmentation algorithm. The procedure toward complete segmentation consists of four steps: pyramid representation, image segmentation, region merging and region projection. First, pyramid representation creates multiresolution images using a wavelet transform. Second, image segmentation segments the lowest-resolution image of the pyramid using a watershed segmentation algorithm. Third, region merging merges the segmented regions using the third-order moment values of the wavelet coefficients. Finally, the segmented low-resolution image with label is projected into a full-resolution image (original image) by inverse wavelet transform. Experimental results of the presented method can be applied to the segmentation of noise or degraded images as well as reduce over-segmentation.

Watershed 알고리즘은 형태학 분야에서 연구되어 온 것으로 단순화된 영상에 대한 경사 영상 화소의 밝기 값을 고도로 생각함으로써 영상을 분할하는데 많이 적용하였다. 하지만, 노이즈에 의해 훼손된 영상을 분할 할 경우, 수 많은 local minima로 인해 영상이 과 분할되고, 분할된 영역을 병합하기 위한 계산 시간 증가의 문제점이 발생된다. 이러한 문제점을 해결하기 위해, 본 논문에서는 웨이블릿 변환을 사용한 watershed 기반 영상 분할의 효율성 증대를 위한 방법을 제안한다. 제안한 영상 분할 방법은 웨이블릿 변환을 이용한 영상의 계층적 표현인 피라미드 표현 단계, watershed 알고리즘을 이용한 영상 분할 단계, 웨이블릿 계수(coefficient)를 이용한 영역 병합 단계와 웨이블릿 역 변환(inverse wavelet transform)을 이용한 영역 투영 단계고 구성된다. 제안된 방법은 노이즈가 포함된 훼손된 영상을 분할 시 발생하는 과 분할문제를 감소시킬 뿐만 아니라, 분할 성능의 개선됨을 알 수 있다.

Keywords

References

  1. K. I. Kim, K. Jung, S. H. Park, H. J. Kim, 'Support Vector Machines for Texture Classification', IEEE Trans on Pattern Analysis and Machine Intelligence, Vol. 24, No. 11, pp. 1542-1550, 2002 https://doi.org/10.1109/TPAMI.2002.1046177
  2. E. Y. Kim, S.W. Hwang, S.H. Park, and H.J. Kim, 'Spatiotemporal Segmentation Using Genetic Algorithms', Pattern Recognition, Vol. 34, No. 10, pp. 2063-2066, 2001 https://doi.org/10.1016/S0031-3203(00)00129-1
  3. J. B. Kim and H. J. Kim, 'Efficient region-based motion segmentation for a video monitoring system', Pattern Recognition Letter, Vol. 24, No. 1-3, pp. 113-128, 2003 https://doi.org/10.1016/S0167-8655(02)00194-0
  4. R. C. Gonzalez and R. E. Woods, Digital Image Processing 2'nd, Prentice Hall, 2002
  5. L. Vincent and P. Soille, 'Watershed in digital space: An efficient algorithm based on immersion simulation', IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 13, No. 6, pp. 583-593, 1991 https://doi.org/10.1109/34.87344
  6. S. Beucher and C. Lantuejoul, 'Use of watershed in contour detection', International Workshop on Image Processing, Real-time edge and motion detection, France, pp. 12-21, 1979
  7. J. B. Kim and H. J. Kim, 'Efficient Image Segmentation Using Wavelet-Based Watershed', Proceedings of the 28th KISS Fall Conference, Vol. 28, No. 2, pp. 472-474, 2001
  8. Y. Tsaig and A. Averbuch, 'Automatic segmentation of moving objects in video sequences: A region labeling approach', IEEE Trans. on Circuits and Syst. for Video Tech., Vol. 12, No. 7, pp. 597-612, 2002 https://doi.org/10.1109/TCSVT.2002.800513
  9. F. Meyer and S. Beucher, 'Morphological segmentation', Journal of Visual communication and Image Representation, Vol. 1, No. 1, pp. 21-46, 1990 https://doi.org/10.1016/1047-3203(90)90014-M
  10. J. Liu and Y. H. Yang, 'Multiresolution color image segmentation', IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 16, No. 7, pp. 689-700, 1994 https://doi.org/10.1109/34.297949
  11. L. Pastor, A. Redriguez, J. M. Espadero, L. Rincon, '3D wavelet-based multiresolution object representation', Pattern Recognition, Vol. 34, No. 12, pp. 2497-2513, 2001 https://doi.org/10.1016/S0031-3203(00)00170-9
  12. J. Z. Wang, J. Li, R. M. Gray, G. Wiederhold, 'Unsupervised multiresolution segmentation for images with low depth of field', IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 23, No. 1, pp. 85-90, 2001 https://doi.org/10.1109/34.899949
  13. J. B. Kim, C. W. Lee, K. M. Lee, T. S. Yun, H. J. Kim, 'Wavelet-based vehicle tracking for Automatic Traffic Surveillance', IEEE Tencon, Vol. 1, pp. 313-316, 2001 https://doi.org/10.1109/TENCON.2001.949604
  14. J. B. Kim and H. J. Kim, 'A Wavelet-Based Watershed Image Segmentation for VOP's Generation', ICPR, Vol. 3, pp. 505-508, 2002 https://doi.org/10.1109/ICPR.2002.1047987
  15. J. B. Kim, H.S. Park, M.H. Park and H. J. Kim, 'A Real-time Motion Segmentation Using Adaptive Thresholding and K-means Clustering', LNAI 2256, Springer-Verlag, pp. 213-224, 2001