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An Empirical Comparison of Three Object Recognition Methods

V. Subbaroyan, Dr.S. Karthik

Abstract


In this paper an attempt has been made to compare three different approaches of Object Recognition namely, Gradient based, Histogram based and Texture based methods. For a realistic approach common household articles with uniform colour properties have been taken up for this study, instead of standard images. An evaluation of the comparative study has been made and the results have been tabulated. We believe that this study will be useful in choosing the appropriate approach in object recognition for service robots. In this paper, we evaluate an object recognition system building on three types of method, Gradient based method, Histogram based method and Texture based method. These methods are suitable for objects of uniform color properties such as cups, cutlery, fruits etc. The system has a significant potential both in terms of service robot and programming by demonstration tasks. This paper outlines the three object recognition system with comparison, and shows the results of experimental object recognition using the three methods.

Keywords


Correlation, Gradient, Histogram, Texture

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References


Bicchi A. and Kumar V., “Robotic grasping and contact: A review,” in Proceedings of the IEEE International Conference on Robotics and Automation, ICRA’00, 2000, pp. 348–353

Nelson R. and Selinger A., “A cubist approach to object recognition,” in ICCV’98, 1998, pp. 614–621.

Bjorkman M. and Kragic D., “Combination of foveal and peripheral vision for object recognition and pose estimation,” Proceedings. IEEE International Conference on Robotics and Automation, ICRA’04, vol. 5, pp. 5135 – 5140, 2004.

Kaiser M. and Dillman R., “Building elementary robot skills from human demonstration,” Proceedings of the IEEE International Conference on Robotics and Automation, v. 3, pp. 2700– 2705, 1996.

Chen J. and Zelinsky A., “Programming by demonstration: removing suboptimal actions in a partially known configuration space,” Proceedings of the IEEE Intl. Conf. onRobotics and Automation (ICRA ’01), vol. 4, pp. 4096–4103, 2001.

Ekvall S. and Kragic D., “Interactive grasp learning based on human demonstration,” in Proc. IEEE/RSJ International Conference on Robotics and automation, ICRA’04, 2004.

Petersson L., Jensfelt P., Tell D., Strandberg M., Kragic D.,and Christensen H. I., “Systems integration for real-world manipulation tasks,”in IEEE International Conference on Robotics and Automation, ICRA 2002, vol. 3, 2002, pp. 2500 – 2505.

Chaumette F., “Image moments: a general and useful set of features for visual servoing,” IEEE Trans. on Robotics, vol. 20 (4), 2004.

Taylor G. and Kleeman L., “Grasping unknown objects with a humanoid robot,” Australiasian Conference on Robotics and Automation, 2002.

Ekvall F. H. S. and Kragic D., “Object recognition and pose estimation for robotic manipulation using color cooccurrence histograms,” in Proc. IEEE/RSJ international Conference Intelligent Robots and Systems,IROS’03, 2003.

Selinger A. and Nelson R., “A perceptual grouping hierarchy for appearance-based 3d object recognition,” CVIU, vol. 76, no. 1, pp. 83– 92, October 1999.

http://www.nada.kth.se/~caputo/cognitive-nipsw03.html.

Arbter K., Snyder W. E., Burkhardt H., and Hirzinger G., “Application of affine-invariant Fourier descriptors to recognition of 3-d objects.” IEEE Trans. Pattern Anal. Mach. Intell., vol.12, no. 7, pp. 640–647, 1990.

Singh R. and Papanikolopoulos N., “Planar shape recognition by shape morphing.” Pattern Recognition, vol. 33, no. 10, pp. 1683–1699, 2000.

Gdalyahu Y. and Weinshall D., “Flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes.” IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, no. 12, pp. 1312–1328, 1999.

Christopoulos Vassilios N. and Schrater Paul,” Handling shape and contact location uncertainty in grasping two dimensional planar objects” Proceedings of the 2007 IEEE/RSJ international Conference on Intelligent Robots and Systems, San Diego, CA, USA, Oct 29 - Nov 2, 2007.


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