  We consider the problem of object recognition in 3D using an ensemble of
attribute-based classifiers. We propose two new concepts to improve
classification in practical situations, and show their implementation in an
approach implemented for recognition from point-cloud data. First, the viewing
conditions can have a strong influence on classification performance. We study
the impact of the distance between the camera and the object and propose an
approach to fuse multiple attribute classifiers, which incorporates distance
into the decision making. Second, lack of representative training samples often
makes it difficult to learn the optimal threshold value for best positive and
negative detection rate. We address this issue, by setting in our attribute
classifiers instead of just one threshold value, two threshold values to
distinguish a positive, a negative and an uncertainty class, and we prove the
theoretical correctness of this approach. Empirical studies demonstrate the
effectiveness and feasibility of the proposed concepts.
