A Combined Hough Transform based Edge Detection and Region Growing Method for Region Extraction

영역 추출을 위한 Hough 변환 기반 에지 검출과 영역 확장을 통합한 방법

  • ;
  • 김용권 (KAIST 전자전산학부 전산학전공) ;
  • 정진완 (KAIST 전자전산학부 전산학전공) ;
  • 이석룡 (한국외국어대학교 산업경영공학과) ;
  • 김덕환 (인하대학교 전자공학과)
  • Published : 2009.08.15

Abstract

Shape features in a content-based image retrieval (CBIR) system are divided into two classes: contour-based and region-based. Contour-based shape features are simple but they are not as efficient as region-based shape features. Most systems using the region-based shape feature have to extract the region firs t. The prior works on region-based systems still have shortcomings. They are complex to implement, particularly with respect to region extraction, and do not sufficiently use the spatial relationship between regions in the distance model In this paper, a region extraction method that is the combination of an edge-based method and a region growing method is proposed to accurately extract regions inside an object. Edges inside an object are accurately detected based on the Canny edge detector and the Hough transform. And the modified Integrated Region Matching (IRM) scheme which includes the adjacency relationship of regions is also proposed. It is used to compute the distance between images for the similarity search using shape features. The experimental results show the effectiveness of our region extraction method as well as the modified IRM. In comparison with other works, it is shown that the new region extraction method outperforms others.

CBIR(Content-based Image Retrieval) 시스템의 질의 처리에 사용되는 모양 특징은 크게 경계 기반과 영역 기반 등 두 가지로 나눌 수 있다. 경계기반 특징은 간단하지만 영역 기반 특징에 비해 효과적이지 않다. 영역 기반 모양 특징을 사용하는 대부분의 시스템은 먼저 영역을 추출해야 한다. 하지만 기존의 영역 기반 시스템들은 구현이 복잡하고, 특히 정확한 영역 추출이 어려우며 영역 간의 위치적인 관계가 거리 모델(distance model)에 반영되어 있지 않다. 본 논문에서는 Canny 에지 검출과 Hough 변환에 기반하여 목표 내부의 에지를 검출하고, 이와 함께 영역확장을 이용하여 목표 물체 내부의 영역을 정확히 추출할 수 있는 방법을 제안하였다. 또한 영역 간의 인접 관계를 이용한 수정된 IRM(Integrated Region Matching) 기법을 제안하였다. 이는 모양 특징을 이용한 유사성 검색에서 영상 간의 거리 모델로서 사용된다. 그리고 실험을 통해 수정된 IRM 기법과 우리의 영역 추출 기법이 효과적임을 보였다. 실험 결과는 새로운 영역 추출 방법이 기존의 다른 방법보다 훨씬 우수함을 보여준다.

Keywords

References

  1. Park, D. K., Jeon, Y. S., and Won, C. S., 'Efficient use of local edge histogram descriptor,' Proc. of the 2000 ACM workshops on Multimedia, pp.51-54, 2000 https://doi.org/10.1145/357744.357758
  2. Pujol, A., Chen, L., 'Line Segment based Edge Feature using Hough Transform,' Proc. of Visualization, Image, and Image Processing (VIIP), 2007
  3. Li, J., Wang, J. Z., and Wiederhold, G., 'IRM: Integrated Region Matching for Image Retrieval,' Proc. of ACM Multimedia, 2000 https://doi.org/10.1145/354384.354452
  4. Chad, C., Megan, T., Serge, B., Joseph, M. H., and Iitendra, M., 'Blobworld: A System for RegionBased Image Indexing and Retrieval,' Proc. of International Conference on Visual Information Systems, pp.509-516, 1999 https://doi.org/10.1007/3-540-48762-X_63
  5. Smith, J. R., and Chang, S. F., 'VisuaISEEk: A Fully Automated Content-Based Image Query System,' Proceedings of the 4th ACM International Conference on Multimedia, pp.87-98, 1997 https://doi.org/10.1145/244130.244151
  6. Ko, B. C., Lee, H. S., and Byun, H., 'Region- based Image Retrieval System Using Efficient Feature Description,' Proc. of the 15th International Conference on Pattern Recognition, pp.283-286, 2000 https://doi.org/10.1109/ICPR.2000.902914
  7. Gonzalez, R. C., and Wood, R. E., 'Digital Image Processing,' Wesley Publishing Company, 2002
  8. S. Liu, M. Zhihong, and S. Rashi, 'Edge Based Region Growing-A New Image Segmentation Method,' Proc. of the 2004 ACM SIGGRAPH international conference on Virtual Reality Continuum and its applications in industry, pp.302-305, 2004 https://doi.org/10.1145/1044588.1044653
  9. Vincent, L., and Soille. P., 'Watersheds in digital spaces: An efficient algorithm based on immersion simulations,' IEEE Transaction on Pattern Analysis and Machine Intelligence, vol.13, no.6, pp.583-598, 1991 https://doi.org/10.1109/34.87344
  10. Chen, Q., Zhou, C., Luo, J. Ming, D., 'Fast Segmentation of High-Resolution Satellite Images Using Watershed Transform Combined with and Efficient Region Merging Approach,' Proc. of International Workshop on Combinatorial Image Analysis, pp.621-630, 2004
  11. Felzenszwalb, P., and Huttenlocher, D., 'Toward Objective Evaluation of Image Segmentation Algorithms,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, no.6, pp.929-944, 2007 https://doi.org/10.1109/TPAMI.2007.1046
  12. Canny, J. 'A computational approach to edge detection,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.8, no.6, pp.679-714, 1986 https://doi.org/10.1109/TPAMI.1986.4767851
  13. Hough, P. V. C., 'A method and means for recognizing complex patterns,' U.S. Patent, 1962
  14. Duda, R. O., and Hart, E. P., 'Use of the Hough Transformation To Detect Lines and Curves In Pictures,' Communications of the ACM, vol.15, no.1, pp.11-15, 1972 https://doi.org/10.1145/361237.361242
  15. Rui, Y., Huang, T. S., Ortega, M., and Mehrotra, S., 'Relevance Feedback: A Power Tool for Interactive Content-Based Image Retrieval,' IEEE Transactions on Circuits and Video Technology, vol.8, no.5, pp.644-655, 1998 https://doi.org/10.1109/76.718510
  16. Jung, F., Li, M. J., Zhang, H. J., Zhang, B., 'Unsupervised Image Segmentation Using Local Homogeneity Analysis,' Proc. of IEEE International Symposium on Circuits and Systems, pp.25-28, 2003