Mahsa Ghafarianzadeh — accepted AI-conference papers

2016

Efficient, dense, object-based segmentation from RGBD video

Mahsa Ghafarianzadeh, Matthew B. Blaschko, Gabe Sibley

ICRA 2016

Spatio-temporal cues offer a rich source of information for inferring structural and semantic scene properties. A particularly useful representation in computer vision is a spatio-temporal video segmentation. Together with motion, knowledge of depth can substantially improve superpixel segmentation.

Cited by 7 Source ↗Scholar ↗

2015

Simultaneous localization, mapping, and manipulation for unsupervised object discovery

Lu Ma, Mahsa Ghafarianzadeh, David Coleman, Nikolaus Correll, Gabe Sibley

ICRA 2015poster

We present an unsupervised framework for simultaneous appearance-based object discovery, detection, tracking and reconstruction using RGBD cameras and a robot manipulator. The system performs dense 3D simultaneous localization and mapping concurrently with unsupervised object discovery. Putative obj…

Cited by 35 Source ↗Scholar ↗