Authors:
Jingyu Hu
1
;
Nobuyuki Kita
2
and
Yasuyo Kita
2
Affiliations:
1
Technology Platform Center, IHI Corporation Technology & Intelligence Integration, Japan
;
2
Intelligent Systems Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
Keyword(s):
Clothing Categorization, Active Recognition, Recognition of Deformable Objects, Automatic Handling of Clothing, Robot Vision.
Abstract:
This paper proposes a method of automatically classifying the category of clothing items by adaptively adjusting common models subject to each observation. In the previous work(Hu and Kita, 2015), we proposed a two-stage method of categorizing a clothing item using a dual-arm robot. First, to alleviate the effect of large physical deformation, the method reshaped a clothing item of interest into one of a small number of limited shapes by using a fixed basic sequence of re-grasp actions. The shape was then matched with shape potential images of clothing category, each of which was configured by combining the clothing contours of various designed items of the same category. However, there was a problem that the shape potential images were too general to be highly discriminative. In this paper, we propose to configure high discriminative shape potential images by adjusting them subject to observation. Concretely, we restrict the contours used for potential images according to simply obs
ervable information. Two series of experiments using various clothing items of five categories demonstrate the effect of the proposed method.
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