Abstract
Barcodes and 2D codes are widely used for various purposes, such as electronic payments and product management. Special code readers, and consumer smartphones can be used to scan codes; thus concerns about fraud and authenticity are important. Embedding watermarks in 2D codes, which allows simultaneous recognition and tamper detection by simply analyzing the captured pattern without requiring an additional device is considered a promising solution. However, smartphone cameras frequently suffer misfocus especially if the target object is too close to the lens, which makes the captured image defocused and results in failure to read watermarks. In this paper, we propose the use of a coded aperture imaging technique to recover watermarks. We have designed a coded aperture that is robust against defocus blur by optimizing the aperture pattern using a genetic algorithm. In addition, we have developed a programmable coded aperture that includes an actual optical process that works in an optimization loop; thus, the complicated effects of the optical aberrations can be considered. Experimental results demonstrate that the proposed method can extend the depth of field for watermark extraction to 3.1 times wider than that of a general circular aperture.
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Notes
- 1.
Note that the watermark can be extracted from the 2D codes printed on papers. Under such scenarios, the 2D code needs to be illuminated.
- 2.
30% error correction is the same capacity as QR code [25].
References
Zhou, C., Nayar, S.: What are good apertures for defocus deblurring? In: IEEE International Conference on Computational Photography (ICCP), pp. 1–8. IEEE (2009)
Levin, A., Fergus, R., Durand, F., Freeman, W.T.: Image and depth from a conventional camera with a coded aperture. ACM Trans. Graph. (TOG) 26, 70 (2007)
Zhou, C., Lin, S., Nayar, S.: Coded aperture pairs for depth from defocus. In: IEEE 12th International Conference on Computer Vision, pp. 325–332 (2009)
Veeraraghavan, A., Raskar, R., Agrawal, A., Mohan, A., Tumblin, J.: Dappled photography: mask enhanced cameras for heterodyned light fields and coded aperture refocusing. ACM Trans. Graph. 26, 69 (2007)
Gottesman, S.R., Fenimore, E.: New family of binary arrays for coded aperture imaging. Appl. Opt. 28, 4344–4352 (1989)
Pramila, A., Keskinarkaus, A., Takala, V., Seppänen, T.: Extracting watermarks from printouts captured with wide angles using computational photography. Multimed. Tools Appl. 76, 16063–16084 (2017)
Pramila, A., Keskinarkaus, A., Seppänen, T.: Increasing the capturing angle in print-cam robust watermarking. J. Syst. Softw. 135, 205–215 (2018)
Raskar, R., Agrawal, A., Tumblin, J.: Coded exposure photography: motion deblurring using fluttered shutter. ACM Trans. Graph. (TOG) 25, 795–804 (2006)
Ng, R., Levoy, M., Brédif, M., Duval, G., Horowitz, M., Hanrahan, P.: Light field photography with a hand-held plenoptic camera. Comput. Sci. Tech. Rep. CSTR 2, 1–11 (2005)
Iwamura, M., Imura, M., Hiura, S., Kise, K.: Recognition of defocused patterns. IPSJ Trans. Comput. Vis. Appl. 6, 48–52 (2014)
Sakuyama, T., Funatomi, T., Iiyama, M., Minoh, M.: Diffraction-compensating coded aperture for inspection in manufacturing. IEEE Trans. Ind. Inform. 11, 782–789 (2015)
Kawamoto, Y., Hiura, S., Miyazaki, D., Furukawa, R., Baba, M.: Design and evaluation of the shape of coded aperture for the recognition of specific patterns (in Japanese). J. Inf. Process. 57, 783–793 (2016)
Masoudifar, M., Pourreza, H.R.: Coded aperture solution for improving the performance of traffic enforcement cameras. Opt. Eng. 55(10)
Hashimoto, W., Sugita, H., Komatsu, S.: Extended depth of field for laser-scanning barcode reader with wavefront coding. In: 2015 20th Microoptics Conference (MOC), pp. 1–2 (2015)
Tisse, C.L., Nguyen, H., Tessières, R., Pyanet, M., Guichard, F.: Extended depth-of-field ( EDoF ) using sharpness transport across colour channels. In: Proceedings of SPIE, Novel Optical Systems Design and Optimization XI, vol. 7061 (2008)
McCloskey, S., Miller, B.: Fast, high dynamic range light field processing for pattern recognition. In: 2016 IEEE International Conference on Computational Photography (ICCP), pp. 1–10 (2016)
Yang, G., Liu, N., Gao, Y.: Two-dimensional barcode image super-resolution reconstruction via sparse representation. In: Proceedings of International Conference on Information Science and Computer Applications (2013)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, Reading (1989)
Kundur, D., Hatzinakos, D.: A robust digital image watermarking method using wavelet-based fusion. In: 4th IEEE International Conference on Image Processing, pp. 544–547 (1997)
Kundurf, D., Hatzinakos, D.: Digital watermarking using multiresolution wavelet decomposition. In: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 5, pp. 2969–2972 (1998)
Ono, S., Maehara, T., Minami, K.: Coevolutionary design of a watermark embedding scheme and an extraction algorithm for detecting replicated two-dimensional barcodes. Appl. Soft Comput. 46(C), 991–1007 (2016)
Sederberg, T.W., Parry, S.R.: Free-form deformation of solid geometric models. SIGGRAPH Comput. Graph. 20, 151–160 (1986)
Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched pareto genetic algorithm for multiobjective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, pp. 82–87 (1994)
Nagahara, H., Zhou, C., Watanabe, T., Ishiguro, H., Nayar, S.K.: Programmable aperture camera using LCoS. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 337–350. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15567-3_25
Information Technology: Automatic identification and data capture techniques - QR Code 2005 bar code symbology specification, ISO 18004 (2000)
Acknowledgements
This study was partially supported by JSPS KAKENHI Grant Numbers JP15H02758 and JP16K12490.
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Hamasaki, H. et al. (2019). A Coded Aperture for Watermark Extraction from Defocused Images. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11366. Springer, Cham. https://doi.org/10.1007/978-3-030-20876-9_15
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