Light field distortion feature for transparent object classification

Yichao Xu, Kazuki Maeno, Hajime Nagahara, Atsushi Shimada, Rin Ichiro Aniguchi

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Local features, such as scale-invariant feature transform (SIFT) and speeded up robust features (SURF), are widely used for describing an object in the applications of visual object recognition and classification. However, these approaches cannot apply to transparent objects made of glass or plastic, as such objects take on the visual features of background scenes, and the appearance of such objects dramatically varies with changes in the scenes. Indeed, transparent objects have the unique characteristic of distorting the background by refraction. In this paper, we use a single-shot light field image as input and model the distortion of the light field caused by the refractive property of a transparent object. We propose a new feature which is called the light field distortion (LFD) feature. The proposed feature is background-invariant so that it is able to describe a transparent object without knowing the texture of the scene. The proposal incorporates this LFD feature into the bag-of-features approach for classifying transparent objects. We evaluated its performance and analyzed the limitations in various settings.

Original languageEnglish
Pages (from-to)122-135
Number of pages14
JournalComputer Vision and Image Understanding
Publication statusPublished - Aug 22 2015

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition


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