The final publication is available at link.springer.com ; We present a system to deal with the problem of classifying garments from a pile of clothes. This system uses a robot arm to extract a garment and show it to a depth camera. Using only depth images of a partial view of the garment as input, a deep convolutional neural network has been trained to classify different types of garments. The robot can rotate the garment along the vertical axis in order to provide different views of the garment to enlarge the prediction confidence and avoid confusions. In addition to obtaining very high classification scores, compared to previous approaches to cloth classification that match the sensed data against a database, our system provides a fast and occlusion-robust solution to the problem. ; Peer Reviewed ; Postprint (author's final draft)
Robot-aided cloth classification using depth information and CNNs
2016-01-01
Miscellaneous
Electronic Resource
English
Àrees temàtiques de la UPC::Informàtica::Robòtica , clothing , image classification , manipulators , neural nets , robot-aided cloth classification , garment extraction , robot arm , depth camera , depth images , deep convolutional neural network , garment classification , occlusion-robust solution , depth information , object detection , Classificació INSPEC::Pattern recognition::Image recognition
DDC: | 629 |
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