Attention-driven object detection and segmentation of cluttered table scenes using 2.5D symmetry

Ekaterina Potapova, Karthik M. Varadarajan, Andreas Richtsfeld, Michael Zillich, Markus Vincze
2014 2014 IEEE International Conference on Robotics and Automation (ICRA)  
The task of searching and grasping objects in cluttered scenes, typical of robotic applications in domestic environments requires fast object detection and segmentation. Attentional mechanisms provide a means to detect and prioritize processing of objects of interest. In this work, we combine a saliency operator based on symmetry with a segmentation method based on clustering locally planar surface patches, both operating on 2.5D point clouds (RGB-D images) as input data to yield a novel
more » ... h to table-top scene segmentation. Evaluation on indoor table-top scenes containing man-made objects clustered in piles and dumped in a box show that our approach to selection of attention points significantly improves performance of state-of-the-art attention-based segmentation methods.
doi:10.1109/icra.2014.6907584 dblp:conf/icra/PotapovaVRZV14 fatcat:rhp3dcsspvcb5o7ywyseatmkbi