Statistical 3D object classification and localization with context modeling

This contribution presents a probabilistic approach for automatic classification and localization of 3D objects in 2D multi-object images taken from a real world environment. In the training phase, statistical object models and statistical context models are learned separately. For the object modeling, the recognition system extracts local feature vectors from training images using the wavelet transformation and models them statistically by density functions. Since in contextual environments a-priori probabilities for occurrence of different objects cannot be assumed to be equal, statistical context modeling is introduced in this work. The a-priori occurrence probabilities are learned in the training phase and stored in so-called context models. In the recognition phase, the system determines the unknown number of objects in a multi-object scene first. Then, the object classification and localization are performed. Recognition results for experiments made on a real dataset with 3240 test images compare the performance of the system with and without consideration of the context modeling.