# Projection-Based Augmented Reality Assistance for Manual Electronic Component Assembly Processes
## 3 Proposed Method
### Authoring Tool  
The main goal of this tool is to generate models which are able to distinguish between the presence and absence of electronic components in the board. This tool is intended to be used before board inspection in case there are any components unknown to the system. In this case, an operator with correct access rights will use this tool to generate the model for this specific component.  
The component catalog is immense, of the order of 10,000 different components, which it is being constantly updated. Furthermore, these components present huge variations in their characteristics such as size, shape, texture and color. In order to tackle this problem, [22] proposed a one-classifier-per-component approach and the definition of a training phase that only needs a single image of a minimum number of components to generate a model. This training phase can be divided into different stages: segmentation, image generation and training.  
* Segmentation: In this stage the operator takes an image of the new referenced component, selecting a foamy material with chromatic contrast to the background. The operator has to place a set of components with the same reference almost covering all the camera field of view. Experiments show that five well distributed components are enough to capture the prospective distortion of  
Figure 2: Hardware setup of the proposed system. The camera and projector are highlighted with light-blue and dark-red rectangles, respectively.  
the camera. When the capture is ready, the segmentation process starts. The first step consists of applying a rough or an approximate segmentation. After this process, a more accurate segmentation is carried out using GrabCut algorithm [23] for improving component segmentation result.
* Image generation: To get a high performance classifier, a substantial number of image samples that include as much component variability as possible, is necessary. In [22], the authors propose generating synthetic images of the components and different backgrounds by applying geometric and photo-metric transformations. This step ensures the robustness of trained classifiers during operation.
* Training: In order to generate the classification model from the generated set of images, the first part is to extract the relevant features from these images. The images of this dataset have a huge variety in terms of background; some of them are totally uniform, while others have numerous pinholes and tracks. For this reason, global features obtained from the whole image should be used instead of focusing on local keypoints. Once features are extracted, a classifier is trained with them, in order to to discriminate between components and background, and it is saved in a database.  
In [22], a study is conducted which compares the accuracy of different combinations of features and classifiers. Training and validation were performed with artificially generated images, whereas testing was performed with real images taken with the proposed setup ensuring performance in real environments. This study was conducted using 21 different components chosen in order to cover a big spectrum of components, ranging from multi-colored big components to uniform small components. In conclusion, a combination of color histograms, histogram Of gradients (HOG) and local binary patterns (LBPs) were chosen as features. Along with a radial-basis function support vector machine (RBF-SVM) as the classifier, this combination achieved more than 90% accuracy in validation and testing. Furthermore, this combination was assured to have low computation time; that is enough for a real-time application.