We present a probabilistic framework for recognizing objects in images of cluttered scenes. Hundreds of objects may be considered and searched in parallel. Each object is learned f...
Abstract. In this paper, we present a new, biologically inspired perceptual feature to solve the selectivity and invariance issue in object recognition. Based on the recent findin...
In this paper, we present a new method for tracking objects with shadows. Traditional motion-based tracking schemes cannot usually distinguish the shadow from the object itself, a...
We present a method to learn object class models from unlabeled and unsegmented cluttered scenes for the purpose of visual object recognition. We focus on a particular type of mode...
We present in this paper a real-time system for shape recognition. The proposed system is a video and multisensor platform that is able to classify the mobile objects evolving in ...