Studien zur Mustererkennung , Bd. 23
Finally, the object features are statistically modeled with the normal distribution and stored in the object models as density functions. Additionally, context modeling is also performed in the training phase. In the recognition phase the system classifies and localizes objects in scenes with real heterogeneous background, whereas the number of objects in a scene is unknown. First, feature vectors are calculated in the scene with the same method as in the training. Second, a maximization algorithm evaluates the learned density functions with the extracted feature vectors and yields classes and poses of objects found in the scene. Experiments made on a real data set with more than 40000 images compare the classification and localization rates for all algorithms discussed in the dissertation and show a very good performance of the system in a real world environment.
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