Light field distortion feature for transparent object recognition

Kazuki Maeno, Hajime Nagahara, Atsushi Shimada, Rin Ichiro Taniguchi

研究成果: Contribution to journalConference article査読

42 被引用数 (Scopus)


Current object-recognition algorithms use local features, such as scale-invariant feature transform (SIFT) and speeded-up robust features (SURF), for visually learning to recognize objects. These approaches though cannot apply to transparent objects made of glass or plastic, as such objects take on the visual features of background objects, and the appearance of such objects dramatically varies with changes in scene background. Indeed, in transmitting light, transparent objects have the unique characteristic of distorting the background by refraction. In this paper, we use a single-shot light field image as an input and model the distortion of the light field caused by the refractive property of a transparent object. We propose a new feature, called the light field distortion (LFD) feature, for identifying a transparent object. The proposal incorporates this LFD feature into the bag-of-features approach for recognizing transparent objects. We evaluated its performance in laboratory and real settings.

ジャーナルProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
出版ステータス出版済み - 2013
イベント26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, 米国
継続期間: 6 23 20136 28 2013

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識


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