Abstract
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.
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Acknowledgments
This work was partially supported by the EU CHIST-ERA I-DRESS project PCIN-2015-147, by the Spanish Ministry of Economy and Competitiveness under project Robinstruct TIN2014-58178-R, and by the CSIC project TextilRob 201550E028.
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Gabas, A., Corona, E., Alenyà, G., Torras, C. (2016). Robot-Aided Cloth Classification Using Depth Information and CNNs. In: Perales, F., Kittler, J. (eds) Articulated Motion and Deformable Objects. AMDO 2016. Lecture Notes in Computer Science(), vol 9756. Springer, Cham. https://doi.org/10.1007/978-3-319-41778-3_2
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DOI: https://doi.org/10.1007/978-3-319-41778-3_2
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