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Multi-View Technique for 3-D Robotic Object Recognition System using Neuro-Fuzzy Method



Table 3: System performance using MF=3 with different step size
Step size ? Maximum accuracy (%)
0.01 83.33
0.05 83.33
0.10 79.44
0.25 77.78

The results show that selecting a proper number of MF and initial step size value will affect the system performance.

The system produces the best result at MF=4, ? =0.10 with 84.44% recognition accuracy. However, MF=2 is adequate to perform a good and fast recognition with a slightly less accuracy at 82.78%.

Table 4: System performance using MF=4 with different step size
Step size Maximum accuracy
0.01 77.78
0.05 81.11
0.10 84.44
0.25 56.67

V. Conclusion
A multiple view 3D robotic object recognition system using neuro-fuzzy system is proposed in this paper. Our experiments show that 3D objects can be modeled and represented by a set of multiple 2D views. In addition, it does not require complex feature sets for 3D object modeling, thus improve processing time for feature extraction stage. Our experiments also proved that neurofuzzy system can perform well in 3D object recognition task although we are using simple feature. While we use simple feature for the purpose of illustration, one may use or combine other feature such as edge, Zernike moment, texture, corner etc to improve the performance of this system. Future work will be the comparison of the approach with other neural networks and/or neuro-fuzzy and actual implementation of the system in a robotic arm object handling and motion planning applications.

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Figure 6: Image scene from different view at reference point

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