Top > IJAT > Contour Object Generation Method for Object Recognition Using ...

single-au.php

IJAT Vol.7 No.2 pp. 182-189
doi: 10.20965/ijat.2013.p0182
(2013)

Paper:

Views over last 60 days: 810

Contour Object Generation Method for Object Recognition Using FPGA

M. Peña-Cabrera*, V. Lomas-Barrie*, I. López-Juárez**,
and R. Osorio-Comparán*

*Instituto de Investigaciones en Matemáticas Aplicadas y Sistemas, Universidad Nacional Autónoma de México, Apdo. Postal 20-726, México D.F., México

**CINVESTAV, Saltillo, Mexico

Received:
December 3, 2012
Accepted:
February 18, 2013
Published:
March 5, 2013
Creative Commons License
Keywords:
robot, vision, manufacture, FPGA, contour
Abstract
The article presents a method for obtaining the contour of an object in real time from non-binarized images for recognition purpose. The contour information is integrated into a descriptive vector named BOF used by a FuzzyARTMAP Artificial Neural Network (ANN) model to learn the object and then recognize it later. In this way, it is possible to obtain a learning process regarding the location and recognition of parts; to communicate to a robot arm the position and orientation information of an object for assembly purposes. Other method to obtain contour using binarized images, is compared with the described method in this paper in order to implement and test both in a Field Programmable Gate Array (FPGA) architecture. Since an ANN can be implemented more efficiently in a parallel structure such as FPGA architecture can supply, it is desirable to implement an efficient algorithm for obtaining the object contour in the same way.
Cite this article as:
M. Peña-Cabrera, V. Lomas-Barrie, I. López-Juárez, and R. Osorio-Comparán, “Contour Object Generation Method for Object Recognition Using FPGA,” Int. J. Automation Technol., Vol.7 No.2, pp. 182-189, 2013.
Data files:
References
  1. [1] G. N. DeSouza and A. C. Kak, “A subsumptive hierarchical and distributed vision-based architecture for smart robotics,” IEEE trans. on Robotics and Automation, 2002.
  2. [2] S. Hutchinson, G. D. Hager, and P. I. Corke, “A tutorial on Visual Servo Control,” IEEE Trans. on Robotics and Automation, Vol.12, No.5, Oct. 1996.
  3. [3] G. M. Bone and D. Capson, “Vision-guided fixtureless assembly of automotive components,” Pergamon, Elsevier Science LTD., Robotics and Computer Integrated Manufacturing, Vol.19, pp 79-87, 2003.
  4. [4] Y. Yoon, G. DeSouza, and A. Kak, “Realtime tracking and Pose estimation for industrial objects using geometric features,” Proc. of the 2003 IEEE Int. Conf. on Robotics Automation, Taipei, Taiwan, Sep. 14-19, pp. 3473-3478, 2003.
  5. [5] S. K. Miller, H. Christensen, and P. Allen, “Automatic grasp planning using shape primitives,” Proc. of the 2003 IEEE Int. Conf. on Robtics Automation, Taipei, Taiwa, Sep. 14-19, pp. 1824-1829, 2003.
  6. [6] E. Bribiesca, “A new Chain Code,” Pergamon, Pattern Recognition, Vol.32, pp. 235-251, 1999.
  7. [7] C. Yüceer and K. Oflazer, “A rotation, scaling and translation invariant pattern classification system,” Pattern Recognition, Vol.26, No.5 pp. 687-710, 1993.
  8. [8] C. S. Langley, “ART FCMAC: a memory efficient neural network for robotic pose estimation,” IEEE Proc. Int. Symp. on Computational Intelligence in Robotics and Automation, Vol.1, pp. 418-423.
  9. [9] A. Bicchi and V. Kumar, “Robotic Grasping and Manipulation,” Springer Verlag, In Ramsete: Articuated and Mobile Robots for Services and Technology, 2001.
  10. [10] Carpenter Gail A. and S. Grossberg, and J. H. Reynolds, “ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by Self-Organizing Neural Network,” Neural Networks, pp. 565-588, 1991.
  11. [11] P. K. Sahoo, S. Soltani, and A. K. C.Wong, “A survey of thresholding techniques,” Computer Vision, Graphics, and Image Processing, Vol.41, Issue 2, pp. 233-260, Feb. 1988, ISSN 0734-189X, 10.1016/0734-189X(88)90022-9.
  12. [12] T. Pun, “A new method for grey-level picture thresholding using the entropy of the histogram,” Signal Processing, Vol.2, Issue 3, pp. 223-237, Jul. 1980.
  13. [13] N. Ahuja and A. Rosenfeld, “A note on the use of second order gray-level statistics for threshold selection,” IEEE Trans Syst. Man Cybern SMC-8, 1978, pp 895-898.
  14. [14] M. Peña-Cabrera and I. Lopez-Juarezl, “A Learning Approach for On Line Object Recognition in Robotic Tasks,” Mexican Int. Conf. on Computer Science ENC, IEEE Computer Society Press, 2004.
  15. [15] J. E. Volder, “The CORDIC Trigonometric Computing Technique,” IRE Trans. on Electronic Computers, pp. 330-334, Sep. 1959.
  16. [16] J. S. Walther, “The Story of Unified Cordic,” The J. of VLSI Signal Processing, Vol.25, No.2, pp. 107-112, 2000.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Oct. 11, 2024