Accuracy improvement of functional attribute recognition by dense CRF considering object shape

Masaki Iizuka, Shuichi Akizuki, Manabu Hashimoto

    研究成果: Article査読

    1 被引用数 (Scopus)

    抄録

    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) conditional random field (CRF), which uses the estimation result of functional attribute for each pixel. However, since this method is optimized from RGB data, it is not able to sufficiently consider the shape of object, which has 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 109 ms in the proposed method.

    本文言語English
    ページ(範囲)56-62
    ページ数7
    ジャーナルElectronics and Communications in Japan
    102
    3
    DOI
    出版ステータスPublished - 2019 3月

    ASJC Scopus subject areas

    • 信号処理
    • 物理学および天文学(全般)
    • コンピュータ ネットワークおよび通信
    • 電子工学および電気工学
    • 応用数学

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