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Quantum Information Processing

, Volume 14, Issue 5, pp 1573–1588 | Cite as

Local feature point extraction for quantum images

  • Yi Zhang
  • Kai Lu
  • Kai Xu
  • Yinghui Gao
  • Richard Wilson
Article

Abstract

Quantum image processing has been a hot issue in the last decade. However, the lack of the quantum feature extraction method leads to the limitation of quantum image understanding. In this paper, a quantum feature extraction framework is proposed based on the novel enhanced quantum representation of digital images. Based on the design of quantum image addition and subtraction operations and some quantum image transformations, the feature points could be extracted by comparing and thresholding the gradients of the pixels. Different methods of computing the pixel gradient and different thresholds can be realized under this quantum framework. The feature points extracted from quantum image can be used to construct quantum graph. Our work bridges the gap between quantum image processing and graph analysis based on quantum mechanics.

Keywords

Quantum image Feature extraction Quantum adder  Quantum image transformation 

Notes

Acknowledgments

The authors appreciate the kind comments and professional criticisms of the anonymous reviewer. This has greatly enhanced the overall quality of the manuscript and opened numerous perspectives geared toward improving the work. This work is supported in part by the National High-tech R&D Program of China (863 Program) under Grants 2012AA01A301 and 2012AA010901, and it is partially supported by National Science Foundation China (NSFC 61103082, 61202333 and CPSF 2012M520392). Moreover, it is a part of Innovation Fund Sponsor Project of Excellent Postgraduate Student (B120601 and CX2012A002).

References

  1. 1.
    Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing. Publishing House of Electronics Industry, Beijing (2002)Google Scholar
  2. 2.
    Feynman, R.: Simulating physics with computers. Int. J. Theor. Phys. 21, 467–488 (1982)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Shor, P.W.: Algorithms for quantum computation: discrete logarithms and factoring. In: Proceeding of 35th Annual Symposium Foundations of Computer Science, pp. 124–134. IEEE Computer Society Press, Los Almitos, CA (1994)Google Scholar
  4. 4.
    Grover, L.: A fast quantum mechanical algorithm for database search. In: Proceedings of the 28th Annual ACM Symposium on the Theory of Computing, pp. 212–219 (1996)Google Scholar
  5. 5.
    Venegas-Andraca, S.E., Bose, S.: Storing, processing and retrieving an image using quantum mechanics. In: Proceeding of the SPIE Conference Quantum Information and Computation, pp. 137–147 (2003)Google Scholar
  6. 6.
    Venegas-Andraca, S.E., Ball, J.L., Burnett, K., Bose, S.: Processing images in entangled quantum systems. Quantum Inf. Process. 9, 1–11 (2010)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Latorre, J.I.: Image compression and entanglement. arXiv:quant-ph/0510031 (2005)
  8. 8.
    Le, P.Q., Dong, F., Hirota, K.: A flexible representation of quantum images for polynomial preparation, image compression, and processing operations. Quantum Inf. Process. 10(1), 63–84 (2011)CrossRefMATHMathSciNetGoogle Scholar
  9. 9.
    Zhang, Y., Lu, K., Gao, Y., Wang, M.: NEQR: a novel enhanced quantum representation of digital images. Quantum Inf. Process. (2013). doi: 10.1007/s11128-013-0567-z
  10. 10.
    Le, P.Q., Iliyasu, A.M., Dong, F., Hirota, K.: Strategies for designing geometric transformations on quantum images. Theor. Comput. Sci. 412, 1406–1418 (2011)CrossRefMATHMathSciNetGoogle Scholar
  11. 11.
    Le, P.Q., Iliyasu, A.M., Dong, F., Hirota, K.: Efficient color transformations on quantum images. J. Adv. Comput. Intell. Intell. Informa. 15(6), 698–706 (2011)Google Scholar
  12. 12.
    Bo, S., Le, P.Q., Iliyasu, A.M., etc.: A multi-channel representation for images on quantum computers using the RGB\(\alpha \) color space. In: Proceedings of the IEEE 7th International Symposium on Intelligent Signal Processing, pp. 160–165 (2011)Google Scholar
  13. 13.
    Fei, Y., Le, P.Q., Iliyasu, A.M., Bo, S.: Assessing the similarity of quantum images based on probability measurements. In: IEEE Congress on Evolutionary Computation, pp. 1–6 (2012)Google Scholar
  14. 14.
    Jiang, N., Wu, W., Wang, L.: The quantum realization of Arnold and Fibonacci image scrambling. Quantum Inf. Process. 13(5), 1223–1236 (2014)CrossRefADSMATHMathSciNetGoogle Scholar
  15. 15.
    Caraiman, S., Manta, V.I.: Histogram-based segmentation of quantum images. Theor. Comput. Sci. 529, 46–60 (2014)CrossRefMATHMathSciNetGoogle Scholar
  16. 16.
    Iliyasu, A.M., Le, P.Q., Dong, F., Hirota, K.: Watermarking and authentication of quantum images based on restricted geometric transformations. Inf. Sci. 186, 126–149 (2012)Google Scholar
  17. 17.
    Zhang, W., Gao, F., Liu, B., Wen, Q., Chen, H.: A watermark strategy for quantum images based on quantum fourier transform. Quantum Inf. Process. doi: 10.1007/s11128-012-0423-6 (2012)
  18. 18.
    Song, X., Wang, S., Liu, S., Abd El-Latif, A.A., Niu, X.: A dynamic watermarking scheme for quantum images using quantum wavelet transform. Quantum Inf. Process. 12(12), 3689–3706 (2013)CrossRefADSMATHMathSciNetGoogle Scholar
  19. 19.
    Zhou, R., Wu, Q., Zhang, M., Shen, C.: Quantum image encryption and decryption algorithms based on quantum image geometric transformations. Int. J. Theor. Phys. 52(6), 1802–1817 (2013)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Zhang, Y., Lu, K., Gao, Y., Xu, K.: A novel quantum representation for log-polar images. Quantum Inf. Process. doi: 10.1007/s11128-013-0587-8 (2013)
  21. 21.
    Edward, R., Drummond, T.: Machine Learning for High-Speed Corner Detection. Computer Vison-ECCV. Springer, Berlin (2006)Google Scholar
  22. 22.
    Marr, D., Hildreth, E.: Theory of edge detection. Proc. R. Soc. Lond. B275, 187–217 (1980)CrossRefADSGoogle Scholar
  23. 23.
    Yang, G., Song, X., Hung, W., et al.: Group theory based synthesis of binary reversible circuits. Lect. Notes Comput. Sci. 3959, 365–374 (2006)CrossRefMathSciNetGoogle Scholar
  24. 24.
    Brayton, R.K., Sangiovanni-Vincentelli, A., McMullen, C., Hachtel, G.: Logic Minimization Algorithms for VLSI Synthesis. Kluwer, Dordrecht (1984)CrossRefMATHGoogle Scholar
  25. 25.
    Mehrotra, R., Sanjay, N., Nagarajan, R.: Corner detection. Pattern Recognit. 23(11), 1223–1233 (1990)CrossRefGoogle Scholar
  26. 26.
    Cheng, K., Tseng, C.: Quantum full adder and subtractor. Electron. Lett. 38(22), 1343–1344 (2002)CrossRefGoogle Scholar
  27. 27.
    Sobel, L.: Camera Models and Machine Perception. Stanford University, CA (1970)Google Scholar
  28. 28.
    Smith, S.M., Brady, J.M.: SUSAN-a new approach to low level image processing. Int. J. Comput. Vis. 23(1), 45–78 (1997)CrossRefGoogle Scholar
  29. 29.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  30. 30.
    Gilles, B., Høyer, P., Tapp, A.: Quantum counting. In: Automata, Languages and Programming, pp. 820–831. Springer, Berlin, Heidelberg (1998)Google Scholar
  31. 31.
    Qiang, X., Yang, X., Wu, J., Zhu, X.: An enhanced classical approach to graph isomorphism using continuous-time quantum walk. J. Phys. A: Math. Theor. 45(4), 045305 (2012)CrossRefADSMathSciNetGoogle Scholar
  32. 32.
    Douglas, B.L., Wang, J.B.: A classical approach to the graph isomorphism problem using quantum walks. J. Phys. A: Math. Theor. 41(7), 075303 (2008)CrossRefADSMathSciNetGoogle Scholar
  33. 33.
    Emms, D., Wilson, R.C., Hancock, E.R.: Graph matching using the interference of continuous-time quantum walks. Pattern Recognit. 42(5), 985–1002 (2009)CrossRefMATHGoogle Scholar
  34. 34.
    Emms, D., Severini, S., Wilson, R.C., Hancock, E.R.: Coined quantum walks lift the cospectrality of graphs and trees. Pattern Recognit. 42(9), 1988–2002 (2009)CrossRefMATHGoogle Scholar
  35. 35.
    Lu, K., Zhang, Y., Gao, Y., et al.: Approximate maximum common sub-graph isomorphism based on discrete-time quantum walk. In: International Conference on Pattern Recognition (2014)Google Scholar
  36. 36.
    Aziz, F., Wilson, R.C., Hancock, E.R.: Backtrackless walks on a graph. IEEE Trans. Neural Netw. Learn. Syst. 24(6), 977–989 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Yi Zhang
    • 1
  • Kai Lu
    • 1
  • Kai Xu
    • 1
  • Yinghui Gao
    • 2
  • Richard Wilson
    • 3
  1. 1.Science and Technology on Parallel and Distributed Processing Laboratory, College of ComputerNational University of Defense TechnologyChangshaChina
  2. 2.College of Electronic Science and EngineeringNational University of Defense TechnologyChangshaChina
  3. 3.Department of Computer ScienceUniversity of YorkYorkUK

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