**1. Introduction**

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How objects are recognised by humans is still an open research field. But, in general there is an agreement that humans recognise objects as established by the similarity principle – among others- of the Gestalt theory of visual perception, which states that things which share visual characteristics such as contour, shape, form, size, colour, texture, value or orientation will be seen as belonging together (Ellis, 1950). This principle applies to human operators; for instance, when an operator is given the task to pick up a specific object from a set of similar objects; the first approaching action will probably be guided solely by visual information clues such as shape similarity. But, if further information is given (i.e. type of surface), then a finer clustering could be accomplished to identify the target object.

The task described above can also be accomplished by automated systems such as industrial robots that can be benefited from the integration of a robust invariant object recognition capability following the above assumptions and by using image features from the object's contour (boundary object information), its shape (i.e. type of curvature or topographical surface information) and form (depth information). These features can be concatenated in order to form an invariant vector descriptor which can be mapped into specific objects using Artificial Intelligence schemes such as Artificial Neural Network (ANN). In previous work, it was demonstrated the feasibility of the approach to learn and recognise multiple 3D working pieces using its contour from 2D images and using a vector descriptor called the Boundary Object Function (BOF) (Peña-Cabrera, et al., 2005). The BOF exhibited invariance with different geometrical pieces, but did not consider surface topographical information. In order to overcome this condition and to have a more robust descriptor, a methodology that includes a shape index using the Shape From Shading (SFS) method (Horn, 1970) is presented as well as the depth information coming from a stereo vision system. The main idea of the approach is to concatenate three vectors, (BOF+SFS+DI) so that not only the contour but also the object's curvature information (shape) and form are taken into account by the ANN.

In this article after presenting related work in Section 2 and original work in Section 3, the contour vector description (BOF), the SFS vector and the stereo disparity map (Depth) are explained in Sections 4, 5 and 6 respectively. A description of the learning algorithm using

The Use of Contour, Shape and Form in an Integrated Neural Approach for Object Recognition 113

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

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

As mentioned earlier, the Boundary Object Function (BOF) method considers only the object's contour to recognise different objects. It is very important to obtain as accurately as possible, metric properties such as area, perimeter, centroid point, and distance from the centroid to the points of the contour of the object. In this section, a description of the BOF

The metric properties for the algorithm are based on the Euclidean distance between two points in the image plane. The first step is to find the object in the image performing a pixellevel scan from top to bottom (first criterion) and left to right (second criterion). For instance, if an object in the image is higher than the others, this object will be considered first. In the event that all objects are from the same height, then the second criterion applies and the

The definition of perimeter is the set of points that make up the shape of the object, in discrete form and is the sum of all pixels that lie on the contour, which can be expressed as:

Equation (1) shows how to calculate the perimeter; the problem lies in finding which pixels in the image belong to the perimeter. For searching purposes, the system calculates the perimeter obtaining the number of points around a piece grouping X and Y points coordinates corresponding to the perimeter of the measured piece in clockwise direction. The perimeter calculation for every piece in the Region of Interest (ROI) is performed after the binarization. Search is always accomplished, as mentioned earlier, from top to bottom and left to right. Once a white pixel is found, all the perimeter is calculated with a search

� � ∑ ∑ pixels �i, j� ∈ contour � � (1)

which is the input to an Artificial Neural Network (ANN).

selected object will be the one located more to the left.

still be confused.

**4. Object's contour** 

method is presented.

**4.1 Metric properties** 

**4.1.1 Perimeter** 

function as it is shown in figure 1.

the FuzzyARTMAP ANN is given in Section 7 followed by Section 8 that describes the results of the proposed integrated approach. Finally, conclusions and future work is described in Section 9.
