# Combined Use of 3D and HSI for the Classification of Printed Circuit Board Components
## 3 Results  
This section shows the steps of the rule-based classification. For a better understanding, an overview of the process is shown in Figure 11. It can be understood as an iterative refinement starting with simple and dominant conditions and continuing in the direction of more complex and composite conditions. The first step of the classification process is based on the knowledge about the spectral behavior of plastics, which is significantly different to those of PCBs. We use this knowledge to separate these two groups and check the average spectrum for each cluster. Since plastic objects are usually made of one material and not of a combination of materials as for PCBs (board and different components), the average spectrum provides a good approach to classify the objects in question (Figure 12). This procedure not only ensures a clean classification of these objects, but also offers advantages in terms of performance, as not every single pixel has to be checked. By assuming that all unclassified objects are objects consisting of composite materials, the classification of clusters can be refined in the next step. The knowledge about the objects allows us to adapt our approach to the characteristics of the objects; in this case, circuit boards. That means, the PCBs are separated into individual parts consisting of the board (a plane surface) and the individual electronic components on the board (ecob). The separation between board and components is based on plane estimation [34], while the components on the board are again processed based on Euclidean distance clustering. Figure 10: Spectral signatures used for the classification. As a result, the rule defined for a category is specific to that category and there is no possibility of confusion because the shapes are distinct enough. Such rules can therefore only be used if the shapes of the spectral curves are all sufficiently different. Next, once again, a rule check of the average spectra for each cluster (board and board components) is carried out. As a result (see example shown in Figure 13) the areas that were divided into boards and components can now be subdivided according to their material properties. In the example dataset, we thus obtain a classification of three different board types, three different types of capacitors and a connector consisting of the material polystyrene (PS). Figure 11: Steps of the rule-based classification process using spectral and spatial features for the rule formation. The geometrical information is not only used for structuring but also supports the classification of components that cannot be classified from their spectral signature only. Figure 12: Example of result of classification (after step 1) of plastic objects by checking average spectra for each cluster. All other existing components do not provide clean spectra that can be used for classification, either because of the black coloring (high absorption) or because of the material composition (metals). However, they are surrounded by detected and classified elements. This relation can be expressed by geometric and topological features allowing to further refine the classification. Figure 14 shows an example of results where a total of four additional groups of components were classified. The four groups include round capacitors, rectangular planar objects (such as microchips), resistors and other components (such as potentiometers and connectors). Especially in this case of classification based on geometric features, it can be seen that the classification of board components is challenging, particularly when dealing with dirty and damaged elements. Even though most of the components have been correctly classified, it can be seen, for example, that one of the connectors has been assigned to the class of radial capacitors (ecob 5).