Abstract:
In this paper we propose a multi-modal object recognition system that uses a two-step hypothesis verification approach to improve runtime efficiency. The system uses loca...Show MoreMetadata
Abstract:
In this paper we propose a multi-modal object recognition system that uses a two-step hypothesis verification approach to improve runtime efficiency. The system uses local and global appearance and shape features, generating many possibly competing hypotheses, which are then verified such that the scene can be optimally explained in terms of recognized object models. The introduced modification in this time consuming step reduces runtime considerably, while maintaining recognition performance. We evaluate recognition performance for various feature extraction modalities on the publicly available Willow Garage RGB-D dataset and show runtime improvements of a factor 2 to 10.
Date of Conference: 04-08 December 2016
Date Added to IEEE Xplore: 24 April 2017
ISBN Information: