**2. Vision systems**

In the last years the use of computer vision systems has enormously increased, especially in the industrial field, for several tasks (Malamas et al., 2003; Chin & Harlow, 1982; Nelson et al., 1996).

Traditionally human experts performed visual inspection and quality control and, in some cases, the human performance are better than the one provided by a machine. On the other hand, human operators are slower than machines, their performance is not constant through time and finding many expert operators is not easy for any industry. Moreover there are several applications where the job may be repetitive and boring (such as target training or robots guidance) or also difficult and dangerous (such as underwater inspection, nuclear industry, chemical industry ...).

Vision system are widely adopted in the control of robots for pick and place operations showing their power in very complex tasks (Sgarbi et al., 2010).

A vision system consists of electronic, optical and mechanical components and it is able to capture, record and process images. Typically it consists of one or more cameras, an optical system, a lighting system, an acquisition system and, finally, an image processing system. The object to be tested is placed in front of cameras and it is properly illuminated. The optical system forms an image on camera sensor which produces in output an electrical signal. Then this signal will be digitized and stored. Finally the image can be analysed with an appropriate software. A general scheme of a typical industrial vision system is shown in Fig.1.

The computational resources are exploited for processing the captured images through suitable analysis and classification software. One or more cameras acquire images under inspection and a lighting system is adopted to illuminate the scene in order to facilitate the acquisition of the image features (Malamas et al., 2003).

The prerequisites for the design and development of a good machine vision system depend on the application field and the task to be reached. An "universal" vision system, i.e. which is capable to fulfil any task in any application, does not exist; only after having established the requirements of the specific application the vision system can be designed.

In the last years computer and machine vision are been connected together for non invasive quality inspection. Machine vision allows to analyse video data, such as data coming from a

Moreover, FIS-based classifiers provide the further advantage of a great flexibility, thanks to the possibility to add rules without affecting the remaining parts of the inference machine. Clearly the preliminary image processing phase should be developed with the aim to extract

The chapter will be organised as follows: a preliminary generic description of the main features of FIS-based image classifiers will be provided, with a particular focus on those general applications where a preliminary ad-hoc image features extraction phase is included. The advantages and limitations of FIS-based classifiers toward other algorithms will be presented and discussed. Afterwards, some case studies will be proposed, where

In the last years the use of computer vision systems has enormously increased, especially in the industrial field, for several tasks (Malamas et al., 2003; Chin & Harlow, 1982; Nelson et

Traditionally human experts performed visual inspection and quality control and, in some cases, the human performance are better than the one provided by a machine. On the other hand, human operators are slower than machines, their performance is not constant through time and finding many expert operators is not easy for any industry. Moreover there are several applications where the job may be repetitive and boring (such as target training or robots guidance) or also difficult and dangerous (such as underwater inspection, nuclear

Vision system are widely adopted in the control of robots for pick and place operations

A vision system consists of electronic, optical and mechanical components and it is able to capture, record and process images. Typically it consists of one or more cameras, an optical system, a lighting system, an acquisition system and, finally, an image processing system. The object to be tested is placed in front of cameras and it is properly illuminated. The optical system forms an image on camera sensor which produces in output an electrical signal. Then this signal will be digitized and stored. Finally the image can be analysed with an appropriate software. A general scheme of a typical industrial vision

The computational resources are exploited for processing the captured images through suitable analysis and classification software. One or more cameras acquire images under inspection and a lighting system is adopted to illuminate the scene in order to facilitate the

The prerequisites for the design and development of a good machine vision system depend on the application field and the task to be reached. An "universal" vision system, i.e. which is capable to fulfil any task in any application, does not exist; only after having established

In the last years computer and machine vision are been connected together for non invasive quality inspection. Machine vision allows to analyse video data, such as data coming from a

the requirements of the specific application the vision system can be designed.

the most suitable features from the images to be fed as input to the fuzzy system.

FIS-based image classifiers are applied in an industrial context.

showing their power in very complex tasks (Sgarbi et al., 2010).

acquisition of the image features (Malamas et al., 2003).

**2. Vision systems** 

industry, chemical industry ...).

system is shown in Fig.1.

al., 1996).

video camera, in order to plan future operations. The automatic systems which carry out visual inspection by means of machine vision are often called *Automatic Visual Inspection Systems* (Jarvis, 1979).

Fig. 1. Scheme of a simple industrial vision system.

Artificial vision is defined as the set of computational techniques which aim to create an approximate model of the three-dimensional world from two-dimensional projections of it. A classic problem concerning artificial vision is to determine whether there are specific objects or activities in the image. Another problem, which is solved by vision systems, is to reconstruct a three dimensional model about the scene to be analysed, given one or more two-dimensional images of it (Klette et al., 1998). The optical techniques which are widely adopted can be divided in two approaches: active methods and passive methods. Active methods regard the case where the scene is radiated by appropriate electromagnetic radiation, while passive methods regard the case where the images of the scene are the real images. Active methods are more expensive than passive ones and they are not always applicable. In contrast, passive methods have lower resolutions than the active ones. Both approaches adopted the visual cues that are used by human vision system to retrieve the depth of the scene projection on the retina, such as blurring and other optical phenomena.

There are many applications of artificial vision in industrial field such as defect detection, robot placement, robot orientation and robot guidance (Lowe & Little, 2005), codes reading, classification. In the last decades the Artificial Vision has evolved into a mature science, which embraces different markets and applications becoming a vital component of advanced industrial systems (Lanzetta, 1998).

Fuzzy Inference Systems Applied to Image Classification in the Industrial Field 247

requirement is introduced to improve the accuracy, in fact it is used to detect edges in the right position. This result is obtained by a process of non-maximum suppression (similar to peak detection) which maintains only the points that are located at the top of a crest of edge data. Finally the third requirement regards the position of a single edge point when a

*High-level features extraction* is used to find shapes in computer images. To better understand this approach let us suppose that the image to be analyzed is represented by a human face. If we want to automatically recognise the face, we can extract the component features, for example the eyes, mouth and the nose. To detect them we can exploit their shape information: for instance, we know that the white part of the eye is ellipsoidal and so on. Shape extraction includes finding the position, the orientation and the size. In many applications the analysis can be helped by the way the shape are placed. In face analysis we

*Thresholding* is a simple shape extraction technique. It is used when the brightness of the shape is known, in fact pixels forming the shape can be detected categorizing pixels according to a fixed intensity threshold. The main advantage lies in its simplicity and in the fact that it requires a low computational effort but this approach is sensitive to illumination change and this is a considerable limit. When the illumination level changes linearly, the adoption of a histogram equalization would provide an image which does not vary; unfortunately this approach is widely sensitive to noise rendering and again the threshold comparison-based approach is impracticable. An alternative technique consists in the subtraction of the image from the background before applying a threshold comparison; this approach requires the a priori knowledge of the background. Threshold comparison and subtraction have the main advantage to be simple and fast but the performances of both

technique are sensitive to partial shape data, noise and variation in illumination.

disadvantage of the proposed approach is the high computational cost.

Another popular shape extraction technique is the so called *Template matching,* which consists in matching a template to an image. The template is a sub-image that represents the shape to be found in the image. The template is centred on an image point and the number of points that match the points in the image are calculated; the procedure is repeated for the whole image and points which led to the best match are the candidates to be the point where the shape is inner the image. Template matching can be seen as a method of parameter estimation, where parameters define the position of the template but the main

Another popular technique which locates shapes in images is the *Hough Transform (*Hough, 1962). This method was introduced by Hough to find bubble tracks and subsequently Rosenfeld (Rosenfeld, 1969) understood its possible advantages as an image processing method. It is widely used to extract lines, circles and ellipses and its main advantage is that it is able to reach the same results than template matching approach but is it faster (Princen et al., 1992). The Hough Transform delineates a mapping from the image points into an accumulator space called Hough space; the mapping is obtained in a computationally efficient manner based on the function that represents the target shape. This approach requires considerable storage and high computational resources but much less than template matching approach. When the shape to be extracted is more complex than lines and circles or the image cannot be partitioned into geometric primitive a *Generalised Hough* 

imagine to find eyes above and the mouth below the nose and so on.

change in brightness occurs.
