**8. Experimental results**

120 Advances in Object Recognition Systems

is given as the difference d = u'- u. If B is the distance between the optical centres, also

The stereoscopic matching algorithm reproduce the human stereopsis process so that a machine, for instance a robot, can perceive the depth of each point in the observed scene and thus is able to manipulate objects, avoid or recreate three-dimensional models. For a pair of stereoscopic images the main goal of these algorithms is to find for each pixel in an image its corresponding pixel in the other image (mating), in order to obtain a disparity map that contains the position difference for each pixel between two images which is proportional to the depth map. To determine the actual depth of the scene it is necessary to take into account the geometry of the stereoscopic system to obtain a metric map. As mating a single pixel is almost impossible, each pixel is represented by a small region that contains it, a socalled window correlation, thereby realizing the correlation between the windows of one image and the other, using the colour of pixels within. Once the disparity map is obtained,

The selection of the ANN for this purpose was based on previous results where the convergence time for some ANN architectures was evaluated during recognition tasks of simple geometrical parts. The assessed networks were Backpropagation, Perceptron and Fuzzy ARTMAP using the BOF vector. Results showed that the FuzzyARTMAP network outperformed the other networks with lower training/testing times (0.838ms/0.0722ms) compared with Perceptron (5.78ms/0.159 ms) and Backpropagation (367.577ms/0.217 ms)

In the Fuzzy ARTMAP (FAM) network there are two modules ARTa and ARTb and an inter-ART module "Map-field" that controls the learning of an associative map from ARTa recognition categories to ARTb categories (Carpenter and Grossberg, 1992). This is

The Map-field module also controls the match tracking of ARTa vigilance parameter. A mismatch between Map field and ARTa category activated by input Ia and ARTb category activated by input Ib increases ARTa vigilance by the minimum amount needed for the system to search for, and if necessary, learn a new ARTa category whose prediction matches the ARTb category. The search initiated by the inter-ART reset can shift attention to a novel cluster of features that can be incorporated through learning into a new ARTa recognition category, which can then be linked to a new ART prediction via associative learning at the

A vigilance parameter measures the difference allowed between the input data and stored patterns. Therefore, this parameter affects the selectivity or granularity of the network prediction. For learning, the FuzzyARTMAP has 4 important factors: Vigilance in the input module (a), vigilance in the output module (b), vigilance in the Map field (ab) and

known as baseline, it can be shown that the depth of P is z = −B / d.

**6.1 Stereoscopic matching algorithms** 

then the histogram of this map is the region of interest.

**7. Learning and recognition** 

(Lopez-Juarez, et al., 2010).

illustrated in Figure 8.

Map-field.

learning rate ().

The experimental results were obtained using two sets of four 3D working pieces of different cross-section: square, triangle, cross and star. One set had its top surface rounded, so that these were referred to as being of rounded type. The other set had a flat top surface and referred to as pyramidal type. The working pieces are showed in figure 9.

The Use of Contour, Shape and Form in an Integrated Neural Approach for Object Recognition 123

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

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

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

The research presented in this article provides an alternative methodology to integrate a robust invariant object recognition system using image features from the object's contour (boundary object information), its form (i.e. type of curvature or topographical surface information) and depth information from a stereo camera. The features can be concatenated in order to form an invariant vector descriptor which is the input to an Artificial Neural

Experimental results were obtained using two sets of four 3D working pieces of different cross-section: square, triangle, cross and star. One set had its surface curvature rounded and the other had a flat surface curvature so that these object were named of pyramidal type. Using the BOF information and training the neural network with this vector it was demonstrated that all pieces were recognised irrespective from its location an orientation within the viewable area. When information was concatenated (BOF + SFS and BOF + Depth), the robustness of the vision system lowered since the recognition rate in both cases was lower than using the BOF vector alone (99.4% and 98.61% respectively). But, using the

20cm x 27cm using different scales and also the slope of the plane was modified.

100% recognition rate varying the scale up to 20% and using a slope of 150.

able to recognize 99.8% from the whole set of objects.

**8.1 Recognition rates** 

Fig. 11. Recognition rate results.

Network (ANN) for learning and recognition purposes.

**9. Conclusions** 

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

Fig. 9. Working pieces.

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 contained 16 values that were repeated 11 times.

Fig. 10. Input vector example.
