Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
권호 표지
DOI QR코드

DOI QR Code

Using Mean Shift Algorithm and Self-adaptive Canny Algorithm for I mprovement of Edge Detection

경계선 검출의 향상을 위한 Mean Shift 알고리즘과 자기 적응적 Canny 알고리즘의 활용

  • 신성윤 (군산대학교 컴퓨터정보공학과) ;
  • 표성배 (인덕대학 컴퓨터 소프트웨어과)
  • Published : 2009.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

Edge detection is very significant in low level image processing. However, majority edge detection methods are not only effective enough cause of the noise points' influence, even not flexible enough to different input images. In order to sort these problems, in this paper an algorithm is presented that has an extra noise reduction stage at first, and then automatically selects the both thresholds depending on gradient amplitude histogram and intra class minimum variance. Using this algorithm, can fade out almost all of the sensitive noise points, and calculate the propose thresholds for different images without setting up the practical parameters artificially, and then choose edge pixels by fuzzy algorithm. In finally, get the better result than the former Canny algorithm.

전경계선 검출은 저수준 영상 처리에서 매우 중요하다. 하지만, 대부분의 경계선 검출 방법들은 노이즈 포인트들의 영향으로 효과적이지 못하며 서로 다른 입력 영상에서도 유연하지 못하다. 이 문제를 해결하기 위하여 본 논문에서는 먼저 외부 노이즈 제거 단계를 제시하였고, 다음으로 기울기 폭 히스토그램과 내부 클래스 최소 변이에 따른 양쪽 임계치의 자동 선택을 제시하였다. 이 알고리즘을 사용하여 민감한 노이즈 포인트들의 대부분을 줄일 수 있었고 실제 파라미터를 인위적으로 세팅하지 않고 서로 다른 영상을 위한 목적 임계치를 계산하며, 퍼지 알고리즘에 의하여 경계선 픽셀들을 선택하였다. 결론적으로 이전의 Canny 알고리즘보다 훨씬 더 좋은 결과를 얻을 수 있었다.

Keywords

References

  1. R.S.Anand, Pradeep Kumar, "Flaw detection in radiographic weld images using morphological approach", NDT&E International, 2006.
  2. Kiranyaz S, Ferreira M, Gabbouj M, "Automatic object extraction over multi scale edge field for multimedia retrieval", IEEE Trans. Image Processing, 2006.
  3. Hongshan Yu, Yaonan Wang, "An improved Canny edge detection algorithm", Engineering and Application of Computer, 2004.
  4. K.W. Bowyer, C. Kranenburg, and S. Dougherty, "dge detector evaluation using empirical ROC curves,"Computer Vision and Image Understanding, Vol. 84, No. 1, pp. 77-03, 2001. https://doi.org/10.1006/cviu.2001.0931
  5. S. Konishi, A. L. Yuille, J. M. Coughlan, and S.C. Zhu, "tatistical edge detection: learning and evaluating edge cues," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 1, pp. 57-4, 2003. https://doi.org/10.1109/TPAMI.2003.1159946
  6. S.Wang, T. Kubota, J. M. Siskind, and J.Wang, "Salient closed boundary extraction with ratio contour,"IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 4, pp. 546-61, 2005. https://doi.org/10.1109/TPAMI.2005.84
  7. I. Weiss and M. Ray, "Model-based recognition of 3D object from single vision," IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 116-128, 2001.
  8. Sukhan Lee, Jongmoo Choi, Daesik Kim, Byungchan Jung, Jaekeun Na, and Hoonmo Kim, "An Active 3D Robot Camera for Home Environment," Proceedings of 4th IEEE Sensors Conference, 2004.
  9. D. Scharstein and R. Szeliski, "High-Accuracy Stereo Depth Maps Using Structured Light," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1. pp. 195-202, 2003.
  10. R. Raskar. K. H. Tan, R. Feris, J. Yu, and M. Turk, "Non-photorealistic Camera: Depth Edge Detection and Stylized Rendering Using Multi-Flash Imaging," Proceedings of ACM SIGGRAPH Conference, Vol. 23, pp. 679-688, 2004.
  11. R. Feris, M. Turk, R. Raskar. K. Tan, and G. Ohashi, "Exploiting Depth Discontinuities for Visionbased Fingerspelling Recognition," IEEE Workshop on Real-Time Vision for Human-Computer Interaction, 2004.