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IJAT Vol.10 No.5 pp. 737-752
doi: 10.20965/ijat.2016.p0737
(2016)

Paper:

Automated Design of Image Recognition Process for Picking System

Taiki Ogata*1,*2,†, Kazuaki Tsujimoto*1, Taigo Yukisawa*1, Yanjiang Huang*3, Tamio Arai*4, Tsuyoshi Ueyama*5, Toshiyuki Takada*5, and Jun Ota*1

*1Research into Artifacts, Center for Engineering (RACE), The University of Tokyo
5-1-5 Kashiwanoha, Kashiwa, Chiba, Japan

Corresponding author

*2Interdisciplinary Graduate School of Science & Engineering, Tokyo Institute of Technology, Kanagawa, Japan

*3School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, P.R. China

*4The Center for Promotion of Educational Innovation, Shibaura Institute of Technology, Tokyo, Japan

*5DENSO WAVE INCORPORATED, Aichi, Japan

Received:
November 4, 2015
Accepted:
August 3, 2016
Published:
September 5, 2016
Keywords:
image recognition system, automated design, image recognition framework, basic processes, picking
Abstract
In this study, we propose an automated design system for an image recognition algorithm applicable to picking work in general and actual factory environments. Considering that an image recognition algorithm design consists of frameworks for selecting a rough recognition method from any of the three basic procedures of pre-processing of contained images, feature-extraction, and discrimination, we formulate it as an optimization problem and propose a random multi-start optimization method by which to derive solutions. We have conducted four types of evaluation experiments for the following four combinations: large or small degrees of similarity in the shape of objects to be recognized and whether they have patterned surfaces. The evaluation experiments show that the proposed design system succeeds in selecting a framework that fits the features of the objects to be recognized and that the designed basic processes have an F measure of 0.9 or more.
Cite this article as:
T. Ogata, K. Tsujimoto, T. Yukisawa, Y. Huang, T. Arai, T. Ueyama, T. Takada, and J. Ota, “Automated Design of Image Recognition Process for Picking System,” Int. J. Automation Technol., Vol.10 No.5, pp. 737-752, 2016.
Data files:
References
  1. [1] T. S. Newman and A. K. Jai, “A survey of automated visual inspection,” Computer Vision and Image Understanding, Vol.61, pp. 231–262, 1995.
  2. [2] H. Golnabi and A. Asadpour, “Design and application of industrial machine vision systems,” Robotics and Computer-integrated Manufacturing, Vol.23, pp. 630–637, 2007.
  3. [3] M. Peña-Cabrera, V. Lomas-Barrie, I. López-Juárez, and R. Osorio-Comparán, “Contour object generation method for object recognition using FPGA,” International Journal of Automation Technology, Vol.7, pp. 182–189, 2013.
  4. [4] P. M. Roth and M. Winter, “Survey of Appearance-based methods for object recognition,” Technical Report, Institute for computer graphics & vision, Graz University of Technology, Vol.15, 2008.
  5. [5] E. N. Malamas, E. G. M. Petrakis, M. Zervakis, L. Petit, and J.-D. Legat, “A survey on industrial vision systems, applications and tools,” Image and vision computing, Vol.21, pp. 171–188, 2003.
  6. [6] M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic imaging, Vol.13, pp. 146–165, 2004.
  7. [7] P. Soille, Morphological Image Analysis: Principles and Applications. Berlin, Heidelberg: Springer-Verlag, 2003.
  8. [8] A. Buades, B. Coll, and J-M Morel, “A review of image denoising algorithms, with a new one,” Multiscale Modeling & Simulation, Vol.4, pp. 490–530, 2005.
  9. [9] N. R. Pal and S. K. Pal, “A review on image segmentation techniques,” Pattern Recognition, Vol.26, pp. 1277–1294, 1993.
  10. [10] Ø. D. Trier, A. K. Jain, and T. Taxt, “Feature extraction methods for character recognition-a survey,” Pattern Recognition, Vol.29, pp. 641–662, 1996.
  11. [11] T. Tuytelaars and K. Mikolajczyk, “Local invariant feature detectors: a survey,” Foundations and Trends in Computer Graphics and Vision, Vol.3, pp. 177-280, 2008.
  12. [12] D. G. Lowe, “Object recognition from local scale-invariant features,” Proceedings of the 7th international conference on IEEE Computer vision, Vol.2, pp. 1150-1157, Sep. 1999.
  13. [13] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, Vol.60, pp. 91-110, 2004.
  14. [14] H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, “Speeded-up robust features (SURF),” Computer Vision and Image Understanding, Vol.110, pp. 346–359, 2008.
  15. [15] C. Tomasi and T. Kaneda, “Detection and tracking of point features,” Carnegie Mellon University Technical Report, CMU-CS-91-132, 1991.
  16. [16] H. Kim, J. Y. Lee, J. H. Kim, J. B. Kim, and W. Y. Han, “Objective recognition and pose estimation using KLT,” Proceedings of 2012 12th International Conference on Control, Automation and Systems (ICCAS), pp. 214–217, 2012.
  17. [17] A. K. Jain, R. P. W. Duin, and J. Mao, “Statistical pattern recognition: A review,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22, pp 4–37, 2000.
  18. [18] R. C. Vogt, Automatic Generation of Morphological Set Recognition Algorithm. New York: Springer-Verlag, 1989.
  19. [19] H. I. Shafeek, E. S. Gadelmawla, A. A. Abdel-Shafy, and I. M. Elewa, “Automatic inspection of gas pipeline welding defects using and expert vision system,” NDT&E International, Vol.37, pp. 301–307, 2004.
  20. [20] M. I. Quintana, R. Polli, and E. Claridge, “Morphological algorithm design for binary image using genetic programming,” Genetic Programming And Evolvable Machines, Vol.7, pp. 81–102, 2006.
  21. [21] T. Nagao and S. Masunaga, “Automatic construction of image transformation processes using genetic algorithm,” IEEE International Conference on Image Processing, Vol.3, pp. 731–734, 1996.
  22. [22] H. Bai, N. Yata, and T. Nagao, “Automatic finding of optimal image processing for extracting concrete image cracks using features ACTIT,” IEEJ Transactions on Electrical and Electronic Engineering, Vol.7, pp. 308-315, 2012.
  23. [23] K. Lillywhite, B. Tippetts, and D. Lee, “Self-tuned evolution-Constructed features for general object recognition,” Pattern Recognition, Vol.45, pp. 241–251, 2012.
  24. [24] K. Lillywhite, D. Lee, B. Tippetts, and J. Archibald, “A feature construction method for general object recognition,” Pattern Recognition, Vol.46, pp. 3300–3314, 2013.
  25. [25] S. S. Bucak, R. Jin, and A. K. Jain, “Multiple kernel learning for visual object recognition: a review,” IEEE transactions on pattern analysis and machine intelligence, Vol.36, pp. 1354–1369, 2014.
  26. [26] http://www.orin.jp/?page_id=73 [accessed Oct. 8, 2015]
  27. [27] M. Ambai and Y. Yoshica, “Card: compact and real-time descriptors,” Proceedings of 2011 IEEE International Conference on Computer Vision (ICCV), pp. 97-104, Nov. 2011.
  28. [28] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and Ian H. Witten, “The WEKA data mining software: an update,” ACM SIGKDD Explorations Newsletter, Vol.11, pp. 10–18, 2009.
  29. [29] J. R. Quinlan, “C4.5: Programs for Machine Learning,” San Mateo, CA: Morgan Kaufmann Publishers, 1993.
  30. [30] T. Ogata, T. Okubo, H. Nagai, M. Yamamoto, M. Sugi, and J. Ota, “A novel algorithm for continuous steel casting scheduling with focus on quality property constraint and slab width maximization,” International Journal of Automation Technology, Vol.9, pp. 235–247, 2015.
  31. [31] J. Y. Chai, T. Sakaguchi, and K. Shirase, “Dynamic controls of genetic algorithm scheduling in supply chain,” International Journal of Automation Technology, Vol.4, pp. 169–177, 2010.

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