Object's Centroid Localization Using Hu-Flusser's Moments Invariant

Abstract:

A combination between two components generally requires screws for assemble them together with high precision and accuracy that is need in industrial application. This research proposes the machine vision technique using Hu-Flussers moments invariant to locate centroids of target screws from a tray for loading instead of the current human vision in manual operation. To validate precision and accuracy template matching is tested in parallel with Hu-Flussers moments invariant. The results show that Hu-Flussers moments invariant is better in terms of precision and have robust ability to exclude outlier too.

• Periodical: Applied Mechanics and Materials (Volume 526)
• Pages: 316-323
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.526.316
• Online since: February 2014
• Authors: Sujin Wanchat*, Supattra Plermkamon, Danaipong Chetchotsak
• Keywords: Flussers Moments Invariant, Hu's Moments Invariant, Machine Vision, Small Screws, Template Matching

References

[1] S. Wanchat, S. Plermkamon, D. Chetchotsak, and N. Homsin, Visual Detection 1. 1 mm Hole Diameter for Attaching Flexible Cable Assembly to Head Suspension Assembly, in International data storage technology conference, Bankok, (2011).

[2] W. -H. Wong, W. -C. Siu, and K. -M. Lam, Generation of moment invariants and their uses for character recognition, Pattern Recognition Letters, vol. 16, pp.115-123, (1995).

DOI: 10.1016/0167-8655(94)00089-l

[3] J. Flusser and T. Suk, Rotation Moment Invariants for Recognition of Symmetric Objects, Image Processing, IEEE Transactions on, vol. 15, pp.3784-3790, (2006).

DOI: 10.1109/tip.2006.884913

[4] S. -D. Wei and S. -H. Lai, Fast Template Matching Based on Normalized Cross Correlation With Adaptive Multilevel Winner Update, Image Processing, IEEE Transactions on, vol. 17, pp.2227-2235, (2008).

DOI: 10.1109/tip.2008.2004615

[5] W. -x. Lai, W. Wang, B. -b. Huang, H. Ren, and T. -l. Shi, Fast Template Matching Based on Local Entropy Difference, " in Image and Signal Processing, 2009. CISP , 09. 2nd International Congress on, 2009, pp.1-5.

DOI: 10.1109/cisp.2009.5302155

[6] M. Mori and K. Kashino, Fast Template Matching Based on Normalized Cross Correlation Using Adaptive Block Partitioning and Initial Threshold Estimation, in Multimedia (ISM), 2010 IEEE International Symposium on, 2010, pp.196-203.

DOI: 10.1109/ism.2010.36

[7] G. R. Baek, Y. H. Mo, J. S. Jeong, J. M. Park, and M. T. Lim, Cognition System Of Bolt Hole Using Template Matching, in the 28th International Symposium on Automation and Robotics in Construction, Korea, (2011).

DOI: 10.22260/isarc2011/0128

[8] R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB, 1 edition ed.: Pearson Prentice Hall, (2004).

[9] M. -K. Hu, Visual pattern recognition by moment invariants, Information Theory, IRE Transactions on, vol. 8, pp.179-187, (1962).

DOI: 10.1109/tit.1962.1057692

[10] H. R. Boveiri, On Pattern Classification Using Statistical Moments, International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 3, (2010).

[11] J. Flusser, T. Suk, and B. Zitova, Moments and Moment Invariants in Pattern Recognition: Wiley, (2009).

[12] J. Flusser, On the independence of rotation moment invariants, Pattern Recognition, vol. 33, pp.1405-1410, (2000).

DOI: 10.1016/s0031-3203(99)00127-2

[13] H. R. Boveiri, Persian Printed Numerals Classification Using Extended Moment Invariants, in International Conference on Image and Vision Computing, Rio de Janeiro, 2010, pp.167-174.