Model-Based Recognition of Domino Tiles Using TGraphs

This paper presents a case study showing that domino tile recognition using a model-based approach delivers results comparable to heuristic or statistical approaches. The knowledge on our models is modeled in TGraphs which are typed, attributed, and ordered directed graphs. Four task-independent rules are defined to create a domain independent control strategy which manages the object recognition. To perform the matching of elements found in the image and elements given by the model, a large number of hypotheses may arise. We designed several belief functions in terms of Dempster-Shafer in order to rate hypotheses emerging from the assignment of image to model elements. The developed system achieves a recall of 89.4% and a precision of 94.4%. As a result we are able to show that model based object recognition is on a competitive basis with the benefit of knowing the belief in each model. This enables the possibility to apply our techniques to more complex domains again, as it was tried and canceled 10 years ago.