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Título

Robot-aided cloth classification using depth information and CNNs

AutorGabás Nova, Antoni; Corona Puyane, Enric; Alenyà, Guillem ; Torras, Carme
Palabras claveGarment classification
Depth images
Deep learning
Fecha de publicación2016
EditorSpringer Nature
CitaciónInternational Conference on Articulated Motion and Deformable Objects: 16-23 (2016)
SerieLecture Notes in Computer Science 9756
ResumenWe 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.
DescripciónTrabajo presentado a la 9th International Conference on Articulated Motion and Deformable Objects (AMDO), celebrada en Palma de Mallorca (España) del 13 al 15 de julio de 2016.
Versión del editorhttps://doi.org/10.1007/978-3-319-41778-3_2
URIhttp://hdl.handle.net/10261/167220
Identificadoresdoi: 10.1007/978-3-319-41778-3_2
isbn: 978-3-319-41778-3
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