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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 6848360, 13 pages
http://dx.doi.org/10.1155/2016/6848360
Research Article

A Dynamic Feature-Based Method for Hybrid Blurred/Multiple Object Detection in Manufacturing Processes

Department of Information Technology and Communication, Shih Chien University, No. 200, University Road, Neimen, Kaohsiung 84550, Taiwan

Received 22 March 2016; Accepted 9 May 2016

Academic Editor: Zhike Peng

Copyright © 2016 Tsun-Kuo Lin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. Weyrich, M. Laurowski, P. Klein, and Y. H. Wang, “A real-time and vision-based methodology for processing 3D objects on a conveyor belt,” International Journal of Systems Applications, Engineering & Development, vol. 5, no. 4, pp. 561–569, 2011. View at Google Scholar
  2. Y.-C. Chen, J.-H. Yu, M.-C. Xie, and F.-J. Shiou, “Automated optical inspection system for analogical resistance type touch panel,” International Journal of Physical Sciences, vol. 6, no. 22, pp. 5141–5152, 2011. View at Google Scholar · View at Scopus
  3. H.-D. Lin and H.-H. Tsai, “Automated quality inspection of surface defects on touch panels,” Journal of the Chinese Institute of Industrial Engineers, vol. 29, no. 5, pp. 291–302, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Rebhi, S. Abid, and F. Fnaeich, “Texture defect detection using local homogeneity and discrete cosine transform,” World Applied Sciences Journal, vol. 31, no. 9, pp. 1677–1683, 2014. View at Google Scholar
  5. W. K. Wong, C. W. M. Yuen, D. D. Fan, L. K. Chan, and E. H. K. Fung, “Stitching defect detection and classification using wavelet transform and BP neural network,” Expert Systems with Applications, vol. 36, no. 2, pp. 3845–3856, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. Z.-H. Huang, W.-J. Li, J. Shang, J. Wang, and T. Zhang, “Non-uniform patch based face recognition via 2D-DWT,” Image and Vision Computing, vol. 37, pp. 12–19, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Kumar, P. Rastogi, and P. Srivastava, “Design and FPGA implementation of DWT, image text extraction,” Procedia Computer Science, vol. 57, pp. 1015–1025, 2015. View at Google Scholar
  8. Y. Zhang, S. Wang, P. Phillips, Z. Dong, G. Ji, and J. Yang, “Detection of Alzheimer's disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC,” Biomedical Signal Processing and Control, vol. 21, pp. 58–73, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. B. Xiao, J.-T. Cui, H.-X. Qin, W.-S. Li, and G.-Y. Wang, “Moments and moment invariants in the Radon space,” Pattern Recognition, vol. 48, no. 9, pp. 2772–2784, 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. L. Diao, J. Peng, J. Dong, and F. Kong, “Moment invariants under similarity transformation,” Pattern Recognition, vol. 48, no. 11, pp. 3641–3651, 2015. View at Publisher · View at Google Scholar · View at Scopus
  11. H. Laga, H. Takahashi, and M. Nakajima, “Spherical wavelet descriptors for content-based 3D model retrieval,” in Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006 (SMI '06), p. 15, IEEE, Matsushima, Japan, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. P. Görgel, A. Sertbas, and O. N. Ucan, “Mammographical mass detection and classification using Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT) hybrid scheme,” Computers in Biology and Medicine, vol. 43, no. 6, pp. 765–774, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Zimbres, R. Alves Batista, and E. Kemp, “Using spherical wavelets to search for magnetically-induced alignment in the arrival directions of ultra-high energy cosmic rays,” Astroparticle Physics, vol. 54, pp. 54–60, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. T. K. Lin, “A novel edge feature description method for blur detection in manufacturing processes,” Journal of Sensors, vol. 2016, Article ID 6506249, 10 pages, 2016. View at Publisher · View at Google Scholar
  15. X. Zhang, X. Li, and Y. Feng, “A medical image segmentation algorithm based on bi-directional region growing,” Optik, vol. 126, no. 20, pp. 2398–2404, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. I. Lázár and A. Hajdu, “Segmentation of retinal vessels by means of directional response vector similarity and region growing,” Computers in Biology and Medicine, vol. 66, pp. 209–221, 2015. View at Publisher · View at Google Scholar · View at Scopus
  17. R. Rouhi, M. Jafari, S. Kasaei, and P. Keshavarzian, “Benign and malignant breast tumors classification based on region growing and CNN segmentation,” Expert Systems with Applications, vol. 42, no. 3, pp. 990–1002, 2015. View at Publisher · View at Google Scholar · View at Scopus
  18. D. Qu, W. Li, Y. Zhang et al., “Support vector machines combined with wavelet-based feature extraction for identification of drugs hidden in anthropomorphic phantom,” Measurement, vol. 46, no. 1, pp. 284–293, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. T. K. Lin, “Adaptive learning method for multiple-object detection in manufacturing,” Advances in Mechanical Engineering, vol. 7, no. 12, pp. 1–12, 2015. View at Publisher · View at Google Scholar
  20. O. Whyte, J. Sivic, A. Zisserman, and J. Ponce, “Non-uniform deblurring for shaken images,” International Journal of Computer Vision, vol. 98, no. 2, pp. 168–186, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  21. Y. Xu, X. Hu, and S. Peng, “Blind motion deblurring using optical flow,” Optik, vol. 126, no. 1, pp. 87–94, 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. T.-K. Lin, “A novel automated inspection approach based on adaptive region-growing image segmentation,” Journal of the Chinese Society of Mechanical Engineers, vol. 35, no. 1, pp. 57–65, 2014. View at Google Scholar · View at Scopus