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Yıl 2016, , 271 - 276, 01.12.2016
https://doi.org/10.18100/ijamec.270410

Öz

Kaynakça

  • [1] G. Divya, and C. Chandrasekhar, “Image Mosaicing Technique for Wide Angle Panorama,” TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 15, pp. 420-429, 2015.
  • [2] M. Lin, G. Xu, X. Ren, and K. Xu, “Cylindrical Panoramic Image Stitching Method Based On Multi-cameras,” The 5th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, Shenyang, China, pp. 1091-1096, June 2015.
  • [3] A. Laraqui, A. Baataoui, A. Saaidi, A. Jarrar, M. Masrar, and K. Satori, “Image mosaicing using voronoi diagram,” Multimedia Tools and Applications, pp. 1-27, 2016.
  • [4] C. M. Huang, S. W. Lin, and J. H. Chen, “Efficient Image Stitching of Continuous Image Sequence with Image and Seam Selections,” IEEE Sensors Journal, vol. 15, pp. 5910-5918, 2015.
  • [5] R. Abraham, and P. Simon, “Review on Mosaicing Techniques in Image Processing,” International Conference on Advanced Computing & Communication Technologies, Rohtak, India, pp. 63-68, April 2013.
  • [6] S. Lee, Y. Park, and D. Lee, “Seamless Image Stitching Using Structure Deformation with HoG Matching,” International Conference on Information and Communication Technology Convergence (ICTC), Jeju, South Korea, pp. 933-935, Oct. 2015.
  • [7] Z. Qui, P. Shi, X. Jiang, D. Pan, C. Feng, and Y. Sha, “Image Stitching and Ghost Elimination Based on Shape-Preserving Half-Projective Warps,” International Conference on Information and Automation, Lijiang, China, pp. 2610-2615, Aug. 2015.
  • [8] A. Laraqui, A. Baataoui, A. Saaidi, A. Jarrar, M. Masrar, and K. Satori, “Image mosaicing using voronoi diagram,” Multimedia Tools and Applications, pp. 1-27, 2016.
  • [9] I. Aydin, M. Karakose, and E. Akin, “An approach for automated fault diagnosis based on a fuzzy decision tree and boundary analysis of a reconstructed phase space,” ISA Transaction, vol. 53, pp. 220-229, 2014.
  • [10] I. Aydin, E. Karakose, M. Karakose, M. T. Gencoglu, and E. Akin, “A New Computer Vision Approach for Active Pantograph Control,” IEEE International Symposium on Innovations in Intelligent Systems and Applications (IEEE INISTA 2013), Albena, Bulgaria, 2013.
  • M. Baygin, and M, Karakose, “A new image stitching approach for resolution enhancement in camera arrays,” 9th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey, pp. 1186-1190, Nov. 2015.
  • P. M. Jain, and V. K. Shandliya, “A Review Paper on Various Approaches for Image Mosaicing,” International Journal of Computational Engineering Research, vol. 3, pp. 106-109, 2013.
  • H. Joshi, and K. Sinha, “A Survey on Image Mosaicing Techniques,” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), vol. 2, pp. 365-369, 2013.
  • D. Ghosh, and N. Kaabouch, “A Survey on Image Mosaicing Techniques,” Journal of Visual Communication and Image Representation, vol. 32, pp. 1-11, 2016.
  • I. K. Sarangi, and S. Nayak, “Image Mosaicing of Panoramic Images,” Bachelor Thesis, National Institute of Technology, Rourkela, 2014.
  • V. K. S. Prathap, S. A. K. Jilani, and P. R. Reddy, “A Critical Review on Image Mosaicing,” International Conference on Computer Communication Informatics (ICCCI), Coimbatore, India, pp. 1-8, Jan. 2016.
  • J. Krizaj, V. Struc, and N. Pvesic, “Adaptation of SIFT Features for Robust Face Recognition,” International Conference on Image Analysis and Recognition (ICIAR), Povoa de Varzim, Portugal, pp. 1-10, June 2010.
  • H. Yetis, M. Baygin, and M. Karakose, “A New Micro Genetic Algorithm Based Image Stitching Approach for Camera Arrays at Production Lines,” 5th International Conference on Manufacturing Engineering and Process (ICMEP), Istanbul, Turkey, 2016.
  • D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, vol. 60, pp. 91-110, 2004.
  • S. Mistry, and A. Patel, “Image Stitching using Harris Feature Detection,” International Research Journal of Engineering and Technology (IRJET), vol. 3, pp. 1363-1369, 2016.
  • A. Levin, A. Zomet, S. Peleg, and Y. Weiss, “Seamless Image Stitching in the Gradient Domain,” Computer Vision (ECCV), pp. 377-389, 2004.
  • Y. Santur, M. Karakose, I. Aydin, E. Akin, “IMU based adaptive blur removal approach using image processing for railway inspection,” International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava, Slovakia, pp. 1-4, May 2016.
  • H. Yetis, and M. Karakose, “Image Mosaicing Based Condition Monitoring Approach for Multi Robots at Production Lines in Industrial Autonomy Systems,” 3rd International Conference on Advanced Technology & Sciences (ICAT 16), Istanbul, Turkey, 2016.

Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems

Yıl 2016, , 271 - 276, 01.12.2016
https://doi.org/10.18100/ijamec.270410

Öz

Continuity
of production is a highly important in the days that manufacturing is becoming
bigger and serial. The mistakes done while producing process cause fail on
products and it may bring about even big losses for the facility. Furthermore,
hitches on robots at production line may also cause crucial damages that may give
rise to high repair costs and discontinuance of production. In this study, it is aimed to obtain
alive bird's eye view map of production lines, which are big and impossible to
be monitored with only one camera, by using multi cameras and stitching
algorithms. Finding the similar scenes of input images, estimation of
homography, warping and blending operations, which are the steps used in feature
based image-stitching algorithms, are applied respectively on images that are
taken by cameras. The assignment of second nearest neighbor distance rate
adaptively makes the results more qualified. After obtaining single stitched image
movement detection is actualized by using the difference of sequential frames, and
anomaly movements are determined. As a result, the robots at the long production
lines can be monitored in one screen, and with processing the obtained image,
faults on robots that may cause damage at non-cheap machines can be handled before
time.

Kaynakça

  • [1] G. Divya, and C. Chandrasekhar, “Image Mosaicing Technique for Wide Angle Panorama,” TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 15, pp. 420-429, 2015.
  • [2] M. Lin, G. Xu, X. Ren, and K. Xu, “Cylindrical Panoramic Image Stitching Method Based On Multi-cameras,” The 5th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, Shenyang, China, pp. 1091-1096, June 2015.
  • [3] A. Laraqui, A. Baataoui, A. Saaidi, A. Jarrar, M. Masrar, and K. Satori, “Image mosaicing using voronoi diagram,” Multimedia Tools and Applications, pp. 1-27, 2016.
  • [4] C. M. Huang, S. W. Lin, and J. H. Chen, “Efficient Image Stitching of Continuous Image Sequence with Image and Seam Selections,” IEEE Sensors Journal, vol. 15, pp. 5910-5918, 2015.
  • [5] R. Abraham, and P. Simon, “Review on Mosaicing Techniques in Image Processing,” International Conference on Advanced Computing & Communication Technologies, Rohtak, India, pp. 63-68, April 2013.
  • [6] S. Lee, Y. Park, and D. Lee, “Seamless Image Stitching Using Structure Deformation with HoG Matching,” International Conference on Information and Communication Technology Convergence (ICTC), Jeju, South Korea, pp. 933-935, Oct. 2015.
  • [7] Z. Qui, P. Shi, X. Jiang, D. Pan, C. Feng, and Y. Sha, “Image Stitching and Ghost Elimination Based on Shape-Preserving Half-Projective Warps,” International Conference on Information and Automation, Lijiang, China, pp. 2610-2615, Aug. 2015.
  • [8] A. Laraqui, A. Baataoui, A. Saaidi, A. Jarrar, M. Masrar, and K. Satori, “Image mosaicing using voronoi diagram,” Multimedia Tools and Applications, pp. 1-27, 2016.
  • [9] I. Aydin, M. Karakose, and E. Akin, “An approach for automated fault diagnosis based on a fuzzy decision tree and boundary analysis of a reconstructed phase space,” ISA Transaction, vol. 53, pp. 220-229, 2014.
  • [10] I. Aydin, E. Karakose, M. Karakose, M. T. Gencoglu, and E. Akin, “A New Computer Vision Approach for Active Pantograph Control,” IEEE International Symposium on Innovations in Intelligent Systems and Applications (IEEE INISTA 2013), Albena, Bulgaria, 2013.
  • M. Baygin, and M, Karakose, “A new image stitching approach for resolution enhancement in camera arrays,” 9th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey, pp. 1186-1190, Nov. 2015.
  • P. M. Jain, and V. K. Shandliya, “A Review Paper on Various Approaches for Image Mosaicing,” International Journal of Computational Engineering Research, vol. 3, pp. 106-109, 2013.
  • H. Joshi, and K. Sinha, “A Survey on Image Mosaicing Techniques,” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), vol. 2, pp. 365-369, 2013.
  • D. Ghosh, and N. Kaabouch, “A Survey on Image Mosaicing Techniques,” Journal of Visual Communication and Image Representation, vol. 32, pp. 1-11, 2016.
  • I. K. Sarangi, and S. Nayak, “Image Mosaicing of Panoramic Images,” Bachelor Thesis, National Institute of Technology, Rourkela, 2014.
  • V. K. S. Prathap, S. A. K. Jilani, and P. R. Reddy, “A Critical Review on Image Mosaicing,” International Conference on Computer Communication Informatics (ICCCI), Coimbatore, India, pp. 1-8, Jan. 2016.
  • J. Krizaj, V. Struc, and N. Pvesic, “Adaptation of SIFT Features for Robust Face Recognition,” International Conference on Image Analysis and Recognition (ICIAR), Povoa de Varzim, Portugal, pp. 1-10, June 2010.
  • H. Yetis, M. Baygin, and M. Karakose, “A New Micro Genetic Algorithm Based Image Stitching Approach for Camera Arrays at Production Lines,” 5th International Conference on Manufacturing Engineering and Process (ICMEP), Istanbul, Turkey, 2016.
  • D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, vol. 60, pp. 91-110, 2004.
  • S. Mistry, and A. Patel, “Image Stitching using Harris Feature Detection,” International Research Journal of Engineering and Technology (IRJET), vol. 3, pp. 1363-1369, 2016.
  • A. Levin, A. Zomet, S. Peleg, and Y. Weiss, “Seamless Image Stitching in the Gradient Domain,” Computer Vision (ECCV), pp. 377-389, 2004.
  • Y. Santur, M. Karakose, I. Aydin, E. Akin, “IMU based adaptive blur removal approach using image processing for railway inspection,” International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava, Slovakia, pp. 1-4, May 2016.
  • H. Yetis, and M. Karakose, “Image Mosaicing Based Condition Monitoring Approach for Multi Robots at Production Lines in Industrial Autonomy Systems,” 3rd International Conference on Advanced Technology & Sciences (ICAT 16), Istanbul, Turkey, 2016.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Research Article
Yazarlar

Hasan Yetiş

Mehmet Karaköse

Yayımlanma Tarihi 1 Aralık 2016
Yayımlandığı Sayı Yıl 2016

Kaynak Göster

APA Yetiş, H., & Karaköse, M. (2016). Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems. International Journal of Applied Mathematics Electronics and Computers(Special Issue-1), 271-276. https://doi.org/10.18100/ijamec.270410
AMA Yetiş H, Karaköse M. Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems. International Journal of Applied Mathematics Electronics and Computers. Aralık 2016;(Special Issue-1):271-276. doi:10.18100/ijamec.270410
Chicago Yetiş, Hasan, ve Mehmet Karaköse. “Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems”. International Journal of Applied Mathematics Electronics and Computers, sy. Special Issue-1 (Aralık 2016): 271-76. https://doi.org/10.18100/ijamec.270410.
EndNote Yetiş H, Karaköse M (01 Aralık 2016) Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems. International Journal of Applied Mathematics Electronics and Computers Special Issue-1 271–276.
IEEE H. Yetiş ve M. Karaköse, “Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems”, International Journal of Applied Mathematics Electronics and Computers, sy. Special Issue-1, ss. 271–276, Aralık 2016, doi: 10.18100/ijamec.270410.
ISNAD Yetiş, Hasan - Karaköse, Mehmet. “Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems”. International Journal of Applied Mathematics Electronics and Computers Special Issue-1 (Aralık 2016), 271-276. https://doi.org/10.18100/ijamec.270410.
JAMA Yetiş H, Karaköse M. Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems. International Journal of Applied Mathematics Electronics and Computers. 2016;:271–276.
MLA Yetiş, Hasan ve Mehmet Karaköse. “Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems”. International Journal of Applied Mathematics Electronics and Computers, sy. Special Issue-1, 2016, ss. 271-6, doi:10.18100/ijamec.270410.
Vancouver Yetiş H, Karaköse M. Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems. International Journal of Applied Mathematics Electronics and Computers. 2016(Special Issue-1):271-6.