Efficient Part Recognition Method in Vision Guided Robots using Orthogonal Moments

  • Authors

    • Bunil Kumar Balabantaray
    • Rabindra Narayan Mahapatra
    • Bibhuti Bhusan Biswal
    2018-12-19
    https://doi.org/10.14419/ijet.v7i4.41.24301
  • Object classification, Zernike moment, Nearest-neighbor, Vision guided robot.

  • Abstract

    The best method in developing automated assembly system is the integration of the machine vision tools with the robot in the assembly platform. Vision system plays an important role in building an autonomous robotic part assembly system. Recognition of the correct part in the assembly line is the key issue while grasping the parts by the robot. The captured images of the parts on the assembly line may be affected by geometric transformation such as rotation, translation, scaling and may be corrupted by Point-Spread-Function (PSF) blurring of camera. In order to recognize parts in such type of condition, three steps are followed in this paper. Firstly, features of the parts are extracted by using combined orthogonal Zernike moment. Secondly, the original part image is reconstructed by using combined orthogonal Zernike moment. For this reconstruction, the moment order is selected by a proposed algorithm. The optimum moment order will maintain a proper balance between the reconstruction capability and sensitivity to noise. Finally, for classification of parts used in assembly nearest-neighbor classifier is used. The suggested technique is implemented in LabVIEW and the simulation is successfully performed in an assembly system with 6-DoF Kawasaki robot.

     

  • References

    1. [1] Hu, M.K., (1962). Visual pattern recognition by moment invariants. IRE transactions on information theory, 8(2), pp.179-187.

      [2] A. Khotanzad and Y.H. Hong, (1990). Invariant image recognition by Zernike moments. IEEE Transactions on pattern analysis and machine intelligence, 12(5), pp.489-497.

      [3] A. Khotanzad and Y. H. Hong, (1990). Invariant image recognition by Zernike moments. IEEE Transactions on pattern analysis and machine intelligence, 12(5), pp.489-497.

      [4] J. Flusser and T. Suk, (1994). A moment-based approach to registration of images with affine geometric distortion. IEEE transactions on Geoscience and remote sensing, 32(2), pp.382-387.

      [5] L. Keyes and A. Winstanley, (2001). Using moment invariants for classifying shapes on large-scale maps. Computers, Environment and Urban Systems, 25(1), pp.119-130.

      [6] C.W. Chong, P. Raveendran and R. Mukundan, (2004). Translation and scale invariants of Legendre moments. Pattern recognition, 37(1), pp.119-129.

      [7] M. Mercimek, K. Gulez and T. V. Mumcu, (2005). Real object recognition using moment invariants. Sadhana, 30(6), pp.765-775.

      [8] J. Flusser, (2006). Moment invariants in image analysis. In proceedings of world academy of science, engineering and technology, 11(2), pp. 196-201.

      [9] K. M. Hosny, (2010). Robust template matching using orthogonal Legendre moment invariants. Journal of computer science, 6(10), p.1083.

      [10] [10] Z. Yang and T. Fang, (2010). On the accuracy of image normalization by Zernike moments. Image and Vision computing, 28(3), pp.403-413.

      [11] B. Chen, H. Shu, H. Zhang, G. Coatrieux, L. Luo and J. L. Coatrieux, (2011). Combined invariants to similarity transformation and to blur using orthogonal Zernike moments. IEEE Transactions on Image Processing, 20(2), pp.345-360.

  • Downloads

  • How to Cite

    Kumar Balabantaray, B., Narayan Mahapatra, R., & Bhusan Biswal, B. (2018). Efficient Part Recognition Method in Vision Guided Robots using Orthogonal Moments. International Journal of Engineering & Technology, 7(4.41), 60-65. https://doi.org/10.14419/ijet.v7i4.41.24301

    Received date: 2018-12-18

    Accepted date: 2018-12-18

    Published date: 2018-12-19