Active Object Recognition in Parametric Eigenspace
Hermann Borotschnig, Lucas Paletta, Manfred Prantl, Axel J. Pinz · 1998
We present an efficient method within an active vision framework for recognizing objects which are ambiguous from certain viewpoints. The system is allowed to reposition the camera to capture additional views and, therefore, to resolve the classification result obtained from a single view. The approach uses an appearance based object representation, namely the parametric eigenspace, and augments it by probability distributions. This captures possible variations in the input images due to errors in the pre-processing chain or the imaging system. Furthermore, the use of probability distributions gives us a gauge to view planning. View planning is shown to be of great use in reducing the number of images to be captured when compared to a random strategy. 1 Introduction Most computer vision systems found in the literature perform object recognition on the basis of the information gathered from a single image. Typically, a set of features is extracted and matched against object ...