**3. Original work**

Classic algorithms such as moment invariants are popular descriptors for image regions and boundary segments; however, computation of moments of a 2D image involves a significant amount of multiplications and additions in a direct method. In many real-time industrial applications, the speed of computation is very important, the 2D moment computation is intensive and involves parallel processing, which can become the bottleneck of the system when moments are used as major features. In addition to this limitation, observing only the piece's contour is not enough to recognise an object since objects with the same contour can still be confused.

In order to cope with this limitation, in this paper a novel method that includes a parameter about the piece contour (BOF), the shape of the object's curvature (SFS) and the depth information from the stereo disparity map (Depth) is presented as main contribution.

The BOF algorithm determines the distance from the centroid to the object's perimeter and the SFS calculates the curvature of the way that light is reflected on parts, whereas the depth information is useful to differentiate similar objects with different height. These features (contour, form and depth) are concatenated in order to form a invariant vector descriptor which is the input to an Artificial Neural Network (ANN).
