# Dynamic Mixed Reality Assembly Guidance Using Optical Recognition Methods
## 6 Discussion
### Assembly Validation Accuracy  
The validation accuracy results show the application effectiveness in validating correctly placed components, whereas it was not as effective for incorrectly placed components. The skewness towards false positive recognition depends on the low number of features in  
Figure 24: Answers to the question “How well did you understand where/how to place the components on the assembly?”.  
Figure 25: Answers to the question “How comfortable did you find the assembly validation procedure?”.  
the model targets. Validation results would be more accurate if models imported in Vuforia contained a higher number of complex features, making it easier for the Model Recognition to decide whether a part has been placed incorrectly. In the case of both the banner and the roof, for example, the only difference when they are placed backwards is the visibility of the holes for the label screws, which is a small feature and, in conditions of imperfect lighting, leads to a false positive recognition.  
Model Target validation step 4 (roof screws) showed that the app tried validation even when the component had not been placed, because of the minimal optical variation between the AMTD guide views with or without the roof screws: whereas the guide views for steps 1, 2, 3, and 5 presented substantial optical differences, the roof screws are mostly occluded by the screw-holes when place. Poor results in steps 6 and 7 were due to the numerous common features in the guide views that allows to validate the model only based on common features--which results in an incorrect validation process. In this case, the validation process can be improved by reducing the number of common features, and further restricting the guide view angles (which would in turn reduce comfort and speed of the application).  
Nevertheless, during these last two steps, the Image Target validation that used the combined position and rotation of the tracked image in relation to the assembly model, achieved correct non-validation 100% of the time. Even though the Model Target validation failed most of the time for those steps, it did produce a perfect overall evaluation, because it needed to be evaluated concurrently with Image Targets.  
The validation accuracy can potentially be improved, providing different guide views in the MTG, but at the same time, the process of creating Model Targets currently presents an underlying limiting issue when validating assembly processes: the black-box functionality of the object recognition algorithm currently does not allow for a straightforward process of creating guide views for validation. When creating the guide views, a routine of good habits can be established, such as limiting the angles and excluding as many common features as possible; however, the process does not exclude the possibility of sub-optimal guide views, which need to undergo a continuous and undesired process of trials-and-errors to be established correctly. Research is still ongoing to solve the issue, and this is one of the main topics that will be addressed in the continuation of the present work.