Object recognition and incremental learning algorithms for a web-based telerobotic system

This paper describes a web-based telerobotic system with two basic capabilities: object recognition and incremental or continual learning. Within this context, the present paper investigates the feasibility of applying several distance-based classifiers to remote object recognition tasks. On the other hand, incremental learning enables the system to maintain a representative set of past training examples that are used together with new data to appropriately modify the currently held knowledge. This can constitute a very significant advantage because it allows a system to continually increase its knowledge and accordingly, to enhance its performance in recognition as it learns. And also, because this capability allows to detect an incorrect or anomalous employment of this particular remote system. The performance of the system is evaluated by means of an empirical analysis addressed to assess two competing goals: computing time and recognition accuracy.

[1]  Ken Taylor,et al.  Internet robots: a new robotics niche , 2000, IEEE Robotics Autom. Mag..

[2]  W. Buxton.  Human-Computer Interaction , 1988, Springer Berlin Heidelberg.

[3]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[4]  László Györfi,et al.  A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.

[5]  Bidyut Baran Chaudhuri,et al.  A new definition of neighborhood of a point in multi-dimensional space , 1996, Pattern Recognit. Lett..

[6]  Peter E. Hart,et al.  The condensed nearest neighbor rule (Corresp.) , 1968, IEEE Trans. Inf. Theory.

[7]  Kenneth Y. Goldberg,et al.  The Mercury Project: a feasibility study for Internet robots , 2000, IEEE Robotics Autom. Mag..

[8]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[9]  Roque Marín,et al.  The Human-Machine Interaction through the UJI Telerobotic Training System , 2003, Robotics and Applications.

[10]  Sebastian Thrun,et al.  Learning One More Thing , 1994, IJCAI.

[11]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

[12]  B. Natarajan.  Machine Learning: A Theoretical Approach , 1992 .

[13]  中澤 真,et al.  Devroye, L., Gyorfi, L. and Lugosi, G. : A Probabilistic Theory of Pattern Recognition, Springer (1996). , 1997 .

[14]  Dennis L. Wilson,et al.  Asymptotic Properties of Nearest Neighbor Rules Using Edited Data , 1972, IEEE Trans. Syst. Man Cybern..

[15]  C. G. Hilborn,et al.  The Condensed Nearest Neighbor Rule , 1967 .