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Attention-driven object detection and segmentation of cluttered table scenes using 2.5D symmetry
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
doi:10.1109/icra.2014.6907584
dblp:conf/icra/PotapovaVRZV14
fatcat:rhp3dcsspvcb5o7ywyseatmkbi