**8.1 Recognition rates**

122 Advances in Object Recognition Systems

Rounded-Cross (RC) Pyramidal-Cross (PC)

Rounded-Star (RSt) Pyramidal-Star (PSt)

The object recognition experiments by the FuzzyARTMAP (FAM) neural network were carried out using the above working pieces. The network parameters were set for fast learning (β = 1) and high vigilance parameter (ρab = 0.9). There were carried out four types of experiments. The first experiment considered only the BOF taking data from the contour of the piece, the second experiment considered information from the SFS algorithm taking into account the reflectance of the light on the surface and the third experiment was performed using the depth information. The fourth experiment used the concatenated vector from the three object descriptors (BOF+SFS+Depth). An example of how an object was coded using the three descriptors is showed in figure 10. Two graphs are presented; the first graph corresponds to the descriptive vector from the Rounded-Square object and the other corresponding to the Pyramidal-square object. The BOF descriptive vector is formed by the 180 first elements (observe that both patterns are very similar since the object's crosssectional shape is the same). Next, there are 175 elements corresponding to the SFS values (every shape corresponding to the 7 index values was repeated 25 times). The following 176 values corresponded to the Depth information obtained for the Disparity Histogram that

Fig. 9. Working pieces.

contained 16 values that were repeated 11 times.

Fig. 10. Input vector example.

Several experiments were defined to test the invariant object recognition capability of the system. For these experiments, the FuzzyARTMAP network was trained with 3 patterns, the objects were located in different orientation and location within a defined working space of 20cm x 27cm using different scales and also the slope of the plane was modified.

The overall results under the above conditions are illustrated in figure 11. The first row corresponded to the recognition rates obtained using only the BOF, SFS, and Depth vector.

It was observed a high recognition rate. For instance, using only the BOF, the system was able to recognize 99.8% from the whole set of objects.

In the second row it is shown the recognition rate using a combination of the BOF+SFS, and BOF+Depth vectors. It is important to notice that the recognition rate in both cases was lower than using the BOF vector alone (99.4% and 98.61%, respectively). In the last experiment, the complete concatenated vector BOF+SFS+Depth vector was used achieving 100% recognition rate varying the scale up to 20% and using a slope of 150.

Fig. 11. Recognition rate results.
