Animated Two-Dimensional Barcode Generation Using Optimization Algorithms – Redesign of Formulation, Operator, and Quality Evaluation
Satoshi Ono, Kensuke Morinaga, and Shigeru Nakayama
Department of Information and Computer Science, Faculty of Engineering, Kagoshima University 1-21-40, Korimoto, Kagoshima, 890-0065, Japan
To improve on our previously proposed but problem-plagued innovation for generating animated and illustrated Quick Response (QR) codes, this paper proposes a method which formulates the animated QR code generation problem as an optimization problem rather than as a set of still QR code decoration problems. The proposed method also uses optimization operators designed for this problem and quality evaluation to maintain natural, smooth movement. Experiments demonstrate that the proposed method can generate animated QR codes involve a maximum of eight illustrations moving inside the code which maintaining decoding feasibility and smooth illustration movement.
Due to a wrong manipulation during the correction of the proofs of the above paper, the running head title (short title) was incorrect. The correct running head title should have read as "Animated Two–Dimensional Barcode Generation."
-  S. Ono, K. Morinaga, and S. Nakayama, “Two-dimensional barcode decoration based on real-coded genetic algorithm,” in Proc. of the 2008 IEEE World Congress on Computational Intelligence (WCCI 2008), pp. 1068-1073, 2008.
-  S. Ono, K. Morinaga, and S. Nakayama, “Barcode design by evolutionary computation,” Artificial Life and Robotics, Vol.13, No.1, pp. 238-241, 2009.
-  S. Ono, K. Morinaga, and S. Nakayama, “Animated two-dimensional barcode generation using optimization algorithms,” in Proc. of Joint 4th Int. Conf. on Soft Computing and Intelligent Systems and 9th Int. Symposium on advanced Intelligent Systems (SCIS&ISIS 2008), pp. 1232-1237, 2008.
-  S. Ono, K. Morinaga, and S. Nakayama, “Animated QR code generation using optimization algorithms,” The Journal of the Society for Art and Science, Vol.8, No.1, pp. 25-34, 2009 (in Japanese).
-  M. Rohs, “Real-world interaction with camera phones,” in Proc. of 2nd Int. Symposium on Ubiquitous Computing Systems 2004, pp. 74-89, 2004.
-  D. E. Goldberg, “Genetic Algorithms in Search, Optimization, and Machine Learning,” Addison Wesley, Reading, 1989.
-  C. Blum and A. Roli, “Metaheuristics in combinatorial optimization: Overview and conceptual comparison,” ACM Computing Surveys, Vol.35, No.3, pp. 268-308, 2003.
-  A. H. Wright, “Genetic algorithms for real parameter optimization,” in Foundations of genetic algorithms, G. J. Rawlins (Ed.), San Mateo, CA: Morgan Kaufmann, pp. 205-218, 1991. Available: citeseer.ist.psu.edu/wright91genetic.html.
-  L. J. Eshelman and J. D. Schaffer, “Real-coded genetic algorithms and interval-schemata,” in Foundations of Genetic Algorithms, Vol.2, pp. 187-202, 1993.
-  L. J. Eshelman, K. E. Mathis, and J. D. Schaffer, “Crossover operator biases: Exploiting the population distribution,” pp. 354-361, 1997.
-  H. Takagi, “Interactive evolutionary computation – fusion of the capabilities of ec optimization and human evaluation,” in Proc. of the IEEE, Vol. 89, pp. 1275-1296, 2001.
-  S. Ono, Y. Hirotani, and S. Nakayama, “Multiple solution search based on hybridization of real-coded evolutionary algorithm and quasi-newton method,” in Proc. of IEEE Congress on Evolutionary Computation, pp. 1133-1140, 2007.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.