Journal of Internet of Things and Convergence (사물인터넷융복합논문지)

The Korea Internet of Things Society (한국사물인터넷학회)
권호 표지
DOI QR코드

DOI QR Code

Edge Detection using Cost Minimization Method

비용 최소화 방법을 이용한 모서리 감지

  • 이동우 (우송대학교 컴퓨터정보학과) ;
  • 이성훈 (백석대학교 컴퓨터공학부)
  • Received : 2021.12.27
  • Accepted : 2022.02.13
  • Published : 2022.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Existing edge discovery techniques only found edges of defined shapes based on precise definitions of edges. Therefore, there are many limitations in finding edges for images of complex and diverse shapes that exist in the real world. A method for solving these problems and discovering various types of edges is a cost minimization method. In this method, the cost function and cost factor are defined and used. This cost function calculates the cost of the candidate edge model generated according to the candidate edge generation strategy. If a satisfactory result is obtained, the corresponding candidate edge model becomes the edge for the image. In this study, a new candidate edge generation strategy was proposed to discover edges for images of more diverse shapes in order to improve the disadvantage of only finding edges of a defined shape, which is a problem of the cost minimization method. In addition, the contents of improvement were confirmed through a simple simulation that reflected these points.

기존의 모서리 감지 기법들은 모서리에 대한 정확한 정의를 바탕으로 하여 정의된 형태의 모서리만을 발견하기 때문에 현실 세계에 존재하는 복잡하고 다양한 형태의 이미지에 대한 모서리를 발견하는데 많은 제약이 따른다. 이러한 문제점을 해결하여 다양한 형태의 모서리를 발견하기 위한 방법이 비용최소화 방법이다. 이 방법에서는 비용함수 및 비용요소를 정의하여 사용하며, 이 비용함수는 후보 모서리 생성 전략에 따라 생성되는 후보 모서리 모형에 대한 비용을 계산하여 만족할 만한 결과가 나타나게 되면 해당 후보 모서리 모형이 해당 이미지에 대한 모서리가 된다. 본 연구에서는 비용최소화 방법의 문제점인 정의된 형태의 모서리만을 발견한다는 단점을 개선하기 위해 좀 더 다양한 형태의 이미지에 대한 모서리를 발견하기 위한 후보 모서리 생성 전략을 제안하였다. 또한 이러한 점을 반영한 간단한 모의실험을 통해 개선 내용을 확인하였다.

Keywords

References

  1. L. Tony, "Edge detection and ridge detection with automatic scale selection", International Journal of Computer Vision, Vol.30, No.2, pp.117-154, 1998. https://doi.org/10.1023/A:1008097225773
  2. L. Tony, "Edge detection", Encyclopedia of Mathematics, EMS Press. 2001.
  3. R. M. Haralick, "Digital Step Edges from Zero Crossing of Second Directional Derivatives", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.Pami-6, No.1, 1984.
  4. M. H. Asghari, B. Jalali, "Physics-inspired image edge detection", 2014 IEEE Global Conference on Signal and Information Processing(GlobalSIP), 2014. DOI: 10.1109/GlobalSIP.2014.7032125.
  5. T. P. Agustin, K. Krissian, A. F. Miguel, S. C. Daniel, "Accurate subpixel edge location based on partial area effect", Image and Vision Computing, Vol.31, Issue 1, pp.72-90, 2013. https://doi.org/10.1016/j.imavis.2012.10.005
  6. Sugata Ghosal, Rajiv Mehrotra, "Orthogonal moment operators for subpixel edge detection", Pattern Recognition, Vol,26, Issue 2, pp.295-306, 1993. https://doi.org/10.1016/0031-3203(93)90038-X
  7. R. D. Jules and T. Takamura, "Alternative Approach for Satellite Cloud Classification: Edge Gradient Application", Advances in Meteorology, 2013. https://doi.org/10.1155/2013/584816
  8. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), 2005.
  9. J. CANNY, "A Computational Approach to Edge Detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.Pami-8, No.6, 1986.
  10. D. Ziou, S Tabbone, "Edge Detection Techniques - An Overview", International Journal of Pattern Recognition and Image Analysis, 8(4):537-559, 1998.
  11. W. Zhang and F. Bergholm, "Multi-Scale Blur Estimation and Edge Type Classification for Scene Analysis", International Journal of Computer Vision, Vol.24, pp.219-250, 1997. https://doi.org/10.1023/A:1007923307644
  12. M. Petrou and J. Kittler. Optimal Edge Detector for Ramp Edges. ieee Transactions on Pattern Analysis and Machine Intel ligence, 13(5) pp. 483-491, 1991. https://doi.org/10.1109/34.134047
  13. R. Mehrotra and S. Zhan. A Computational Approach to Zero-Crossing-Based Two Dimensional Edge Detection. CVGIP: Graphical Models and Image Processing, Vol.58, pp.1-17, 1996. https://doi.org/10.1006/ciun.1993.1028
  14. J. F. Canny. "A Computational Approach to Edge Detection", IEEE Transactions on Pattern Analysis and Machine Intel ligence, Vol.8, No.6, pp.679-698, 1986. https://doi.org/10.1109/TPAMI.1986.4767851
  15. D. J. Williams and M. Shah. "Edge Characterization Using Normalized Edge Detector", Computer Vision, Graphics and Image Processing, Vol.55, pp.311-318, 1993.
  16. S. Tabbone and D. Ziou. "Elimination of False Edges by Separation and Propagation of Thresholds", In 13th Conference on Signal Processing and Images, 1991.
  17. H. L. Tan and S. B. Gelfand, "A Cost Minimization Approach to Edge Detection using Simulated Annealing", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No.1, 1991.
  18. S. H. Lee and K. M. Cho, "A Study on the Reality of IoT Device and Service Information Gap in the Era of Digital Transformation," Journal of the Korean Internet of Things Society Vol.7, No.1, pp.79-89, 2021.