Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Triqubit-State Measurement-Based Image Edge Detection Algorithm

  • Wang, Zhonghua (Key Laboratory of Nondestructive Testing, Ministry of Education, Nanchang Hangkong University) ;
  • Huang, Faliang (School of Information Engineering, Nanchang Hangkong University)
  • Received : 2017.07.05
  • Accepted : 2018.02.08
  • Published : 2018.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Aiming at the problem that the gradient-based edge detection operators are sensitive to the noise, causing the pseudo edges, a triqubit-state measurement-based edge detection algorithm is presented in this paper. Combing the image local and global structure information, the triqubit superposition states are used to represent the pixel features, so as to locate the image edge. Our algorithm consists of three steps. Firstly, the improved partial differential method is used to smooth the defect image. Secondly, the triqubit-state is characterized by three elements of the pixel saliency, edge statistical characteristics and gray scale contrast to achieve the defect image from the gray space to the quantum space mapping. Thirdly, the edge image is outputted according to the quantum measurement, local gradient maximization and neighborhood chain code searching. Compared with other methods, the simulation experiments indicate that our algorithm has less pseudo edges and higher edge detection accuracy.

Keywords

E1JBB0_2018_v14n6_1331_f0001.png 이미지

Fig. 1. QSP framework.

E1JBB0_2018_v14n6_1331_f0002.png 이미지

Fig. 2. A 3×3 neighborhood window.

E1JBB0_2018_v14n6_1331_f0003.png 이미지

Fig. 3. Gray level distribution on both sides of the central pixel’s edge direction.

E1JBB0_2018_v14n6_1331_f0004.png 이미지

Fig. 4. Edge detection result comparisons. (a) Edge detection in delamination image. (b) Edge detection in inclusion image.

E1JBB0_2018_v14n6_1331_f0005.png 이미지

Fig. 5. Edge detection procedure of our method. (a) Delamination image, (b) PM smoothing image, (c)triqubit edge location image, (d) local gradient maximization image, and (e) 8-neighborhood edge connection image.

Table 1. Edge detection evaluation datum of delamination defect image

E1JBB0_2018_v14n6_1331_t0001.png 이미지

Table 2. Edge detection evaluation datum of inclusion defect image

E1JBB0_2018_v14n6_1331_t0002.png 이미지

References

  1. P. Melin, C. I. Gonzalez, J. R. Castro, O. Mendoza, and O. Castillo, "Edge-detection method for image processing based on generalized type-2 fuzzy logic," IEEE Transactions on Fuzzy Systems, vol. 22, no. 6, pp. 1515-1525, 2014. https://doi.org/10.1109/TFUZZ.2013.2297159
  2. C. S. Tseng and J. H. Wang, "Perceptual edge detection via entropy-driven gradient evaluation," IET Computer Vision, vol. 10, no. 2, pp. 163-171, 2016. https://doi.org/10.1049/iet-cvi.2015.0030
  3. S. Ansari, S. G. Prabhu, N. Kini, G. Hegde, and Y. Haider, "A survey on conventional edge detection techniques," International Journal of Scientific Research in Computer Science Applications and Management Studies, vol. 3, no. 1, pp. 1-4, 2014.
  4. Y. X. Zhao, J. M. Han, Z. Y. Yang, W. J. Li, and J. Q. Wang, "Recognition of welded joint contour by edge detection and mathematical morphology," Journal of Northeastern University, vol. 35, pp. 115-118, 2014.
  5. M. Gonzalez-Hidalgo, S. Massanet, A. Mir, and D. Ruiz-Aguilera, "On the choice of the pair conjunction-implication into the fuzzy morphological edge detector," IEEE Transactions on Fuzzy Systems, vol. 23, no. 4, pp. 872-884, 2015. https://doi.org/10.1109/TFUZZ.2014.2333060
  6. R. S. Ingarden, "Quantum information theory," Reports on Mathematical Physics, vol. 10, no. 1, pp. 43-72, 1976. https://doi.org/10.1016/0034-4877(76)90005-7
  7. P. E. Frenkel and M. Weiner, "Classical information storage in an n-level quantum system," Communications in Mathematical Physics, vol. 340, no. 2, pp. 563-574, 2015. https://doi.org/10.1007/s00220-015-2463-0
  8. C. Shukla and A. Pathak, "Hierarchical quantum communication," Physics Letters A, vol. 377, no. 19-20, pp. 1337-1344, 2013. https://doi.org/10.1016/j.physleta.2013.04.010
  9. Y. C. Eldar and A. V. Oppenheim, "Quantum signal processing," IEEE Signal Processing Magazine, vol. 19, no. 6, pp. 12-32, 2002. https://doi.org/10.1109/MSP.2002.1043298
  10. K. Xie, Research on Quantum Derivative Image Processing Method. Changsha, China: Central South University, 2007.
  11. Y. Dang, N. Jiang, H. Hu, and W. Zhang, "Analysis and improvement of the quantum image matching," Quantum Information Processing, vol. 16, article no. 269, 2017.
  12. K. Xie and W. Xu, "Quantum-inspired image decomposition and edge detection," Jisuanji Yingyong/Journal of Computer Applications, vol. 33, no. 4, pp. 1089-1091, 2013. https://doi.org/10.3724/SP.J.1087.2013.01089
  13. X. W. Fu, M. Y. Ding, C. P. Zhou, C. Cai, and Y. G. Sun, "Research on image enhancement algorithms of medical images based on quantum probability statistics," Acta Electronica Sinica, vol. 38, no. 7, pp. 1590- 1596, 2010.
  14. Z. Mbarki, H. Seddik, S. Tebini, and E. B. Braiek, "A new rapid auto-adapting diffusion function for adaptive anisotropic image de-noising and sharply conserved edges," Computers & Mathematics with Applications, vol. 74, no. 8, pp. 1751-1768, 2017. https://doi.org/10.1016/j.camwa.2017.06.026
  15. G. Wang, L. Xiao, and A. He, "An improved computing method for the image edge detection," Chinese Optics Letters, vol. 5, no. 2, pp. 79-81, 2007.
  16. A. F. de Araujo, C. E. Constantinou, and J. M. R. Tavares, "New artificial life model for image enhancement," Expert Systems with Applications, vol. 41, no. 13, pp. 5892-5906, 2014. https://doi.org/10.1016/j.eswa.2014.03.029