物体形状を考慮したdense CRFによる機能属性認識の高精度化

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2018/09/01

タイトル(英語): Accuracy Improvement of Functional Attribute Recognition by Dense CRF Considering Object Shape

著者名: 飯塚　正樹（中京大学大学院 工学研究科），秋月　秀一（中京大学大学院 工学研究科／慶應義塾大学），橋本　学（中京大学大学院 工学研究科）

著者名(英語): Masaki Iizuka (Graduate School of Engineering, Chukyo University), Shuichi Akizuki (Graduate School of Engineering, Chukyo University/Keio University), Manabu Hashimoto (Graduate School of Engineering, Chukyo University)

キーワード: 機能属性，アフォーダンス，生活支援ロボット，dense CRF　　functional attribute，affordance，partner robot，dense CRF

要約(英語): In this paper, we propose a method to recognize functional attributes of everyday objects for vision system of partner robots. On the related research, there is a method to optimize recognition result with dense (fully connected) CRF which use the estimation result of functional attribute for each pixels. However, since this method is optimized from RGB data, it isn't able to sufficiently consider the shape of object, which have a relationship with the function attribute. In the proposed method, the recognition accuracy of functional attributes is improved by considering the object shape with the dense CRF describing the three - dimensional positional relationship. As a result of the experiment, the recognition rate of the proposed method is 77.0 %, which is 3.8 % higher than the related method. In addition, we confirmed that the processing speed is high as a side effect by reducing processing cost by oversegmentation of input data and using high speed identification by Random Forests. The mean processing speed per an object was 109ms in the proposed method.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.138 No.9 （2018） 特集Ⅰ：知能メカトロニクス分野と連携する知覚情報技術 特集Ⅱ：国際会議ICESS 2017

本誌掲載ページ: 1088-1093 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/138/9/138_1088/_article/-char/ja/