An Effective Method for Fast Hub Location Based on Design-Template

Abstract:

In order to solve the hub workpiece recogni tion, location and precision grabbing problem before fine processing, a method of hub workpiece recognition and lo cation based on design-template is proposed. In this method, create design-template according to the design size and external shape of the hub workpiece, extract some invariants as features, obtain similar ity according to the corresponding features rec ognized and extracted from target image, adjust and align the image in small range based on the smallest similarity criter ia, then achieve target location. Experiments show that this method can precisely locate the key pa rts of the hub workpiece an d the recognition accuracy is greater than 98%, error accuracy of the position and angle, resp ectively, are within 2 pixels and 0.08 degrees, moreover it has less amount of calculation. It could satisfy the actual need of production line.

• Periodical: Advanced Materials Research (Volume 645)
• Pages: 329-333
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.645.329
• Online since: January 2013
• Authors: Lian Qiang Niu, Zhi Tian Li
• Keywords: Design-Template, Hub Location, Image Processing, Matching, Pattern Recognition

References

[1] Y. Wang, W. P. Fu, G. W. Yuan and H. S. Chen: Research on automatic vision positioning and recognizing system for workpieces, Computer Engineering and Applications, Vol. 80-83 (2009).

[2] F. Yang, W. J. Liu, R. B. Xia and Y. J. Huang: A method of automatic wheel identify and classify, Microcomputer Information, Vol. 164-166 (2010).

[3] Q. S. Zhu, S. Q. Yang and F. Liu: Image edge abstract-based fast template matching, Application Research of Computers, Vol. 792-794 (2009).

[4] J. Sun, H. B. Gu, X. L. Qin and N. Zhou: An image matching algorithm using robust Hausdorff distance, Journal of Image and Graphics, Vol. 761-767 (2008).

[5] X. F. Leng, J. Y. Liu, Z Xiong and G. H. Xing: Application of weighted Hausdorff distance algorithm in SAR/INS scene matching, Control and Decision, Vol. 42-45 (2006).

[6] X. Y. Wang and T.T. Cheng, Research on industrial robot intelligent grasp based on monocular vision, Machinery Design & Manufacture, Vol. 135-136 (2011).

[7] Jain, A. K., Zhong, Y. and Lakshmanan, S.: Object matching using deformable template, IEEE Pattern Analysis and Machine Intelligence, Vol. 267-269 (1996).

DOI: 10.1109/34.485555

[8] Dao, M. S., Natale, F. D. and Massa, A.: Edge Potential function (EPF) and genetic algorithms (GA) for edge-based matching of visual object, IEEE Multimedia Magazine, Vol. 120-124 (2007).

DOI: 10.1109/tmm.2006.886371

[9] W. K. Yang, M. B. Ren, J. Y. Yang: Object area algorithms based on chain code in digital image, Computer Engineering, Vol. 30-33 (2008).