**9. Conclusions**

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 Network (ANN) for learning and recognition purposes.

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

**Section 4** 

**Applications**

complete concatenated vector BOF+SFS+Depth achieved 100% recognition rate invariant to scale up to 20% and also invariant to the inclination of the plane up to 150. Further tests were conducted but the recognition was lower since for instance, increasing the slope angle it contributed to distort the contour as detected by the BOF hence making the recognition rate very sensitive.
