Name
Abstract | ||
|---|---|---|
Category and pose recognition of highly deformable objects is considered a challenging problem in computer vision and robotics. In this study, we investigate recognition and pose estimation of garments hanging from a single point, using a hierarchy of deep convolutional neural networks. The adopted framework contains two layers. The deep convolutional network of the first layer is used for classifying the garment to one of the predefined categories, whereas in the second layer a category specific deep convolutional network performs pose estimation. The method has been evaluated using both synthetic and real datasets of depth images and an actual robotic platform. Experiments demonstrate that the task at hand may be performed with sufficient accuracy, to allow application in several practical scenarios. |
Year | Venue | Field |
|---|---|---|
2015 | PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS (ICAR) | Computer vision,Pattern recognition,Convolutional neural network,Computer science,3D pose estimation,Pose,Artificial intelligence,Articulated body pose estimation,Deep learning,Hierarchy,Robotics |
DocType | Citations | PageRank |
Conference | 6 | 0.45 |
References | Authors | |
21 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ioannis Mariolis | 1 | 33 | 4.86 |
| Georgia Peleka | 2 | 17 | 1.77 |
| Andreas Kargakos | 3 | 39 | 3.63 |
| Sotiris Malassiotis | 4 | 335 | 27.46 |