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Local feature point extraction for quantum images

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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.

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References

  1. Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing. Publishing House of Electronics Industry, Beijing (2002)

    Google Scholar 

  2. Feynman, R.: Simulating physics with computers. Int. J. Theor. Phys. 21, 467–488 (1982)

    Article  MathSciNet  Google Scholar 

  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)

  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)

  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)

  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)

    Article  MathSciNet  Google Scholar 

  7. Latorre, J.I.: Image compression and entanglement. arXiv:quant-ph/0510031 (2005)

  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)

    Article  MATH  MathSciNet  Google Scholar 

  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. 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)

    Article  MATH  MathSciNet  Google Scholar 

  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)

  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)

  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)

  14. Jiang, N., Wu, W., Wang, L.: The quantum realization of Arnold and Fibonacci image scrambling. Quantum Inf. Process. 13(5), 1223–1236 (2014)

    Article  ADS  MATH  MathSciNet  Google Scholar 

  15. Caraiman, S., Manta, V.I.: Histogram-based segmentation of quantum images. Theor. Comput. Sci. 529, 46–60 (2014)

    Article  MATH  MathSciNet  Google Scholar 

  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)

  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. 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)

    Article  ADS  MATH  MathSciNet  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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. Edward, R., Drummond, T.: Machine Learning for High-Speed Corner Detection. Computer Vison-ECCV. Springer, Berlin (2006)

    Google Scholar 

  22. Marr, D., Hildreth, E.: Theory of edge detection. Proc. R. Soc. Lond. B275, 187–217 (1980)

    Article  ADS  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  24. Brayton, R.K., Sangiovanni-Vincentelli, A., McMullen, C., Hachtel, G.: Logic Minimization Algorithms for VLSI Synthesis. Kluwer, Dordrecht (1984)

    Book  MATH  Google Scholar 

  25. Mehrotra, R., Sanjay, N., Nagarajan, R.: Corner detection. Pattern Recognit. 23(11), 1223–1233 (1990)

    Article  Google Scholar 

  26. Cheng, K., Tseng, C.: Quantum full adder and subtractor. Electron. Lett. 38(22), 1343–1344 (2002)

    Article  Google Scholar 

  27. Sobel, L.: Camera Models and Machine Perception. Stanford University, CA (1970)

    Google Scholar 

  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)

    Article  Google Scholar 

  29. Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, pp. 147–151 (1988)

  30. Gilles, B., Høyer, P., Tapp, A.: Quantum counting. In: Automata, Languages and Programming, pp. 820–831. Springer, Berlin, Heidelberg (1998)

  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)

    Article  ADS  MathSciNet  Google Scholar 

  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)

    Article  ADS  MathSciNet  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

  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)

    Article  Google Scholar 

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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).

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Correspondence to Yi Zhang.

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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).

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Zhang, Y., Lu, K., Xu, K. et al. Local feature point extraction for quantum images. Quantum Inf Process 14, 1573–1588 (2015). https://doi.org/10.1007/s11128-014-0842-7

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