TransCut: Transparent Object Segmentation from a Light-Field Image

The segmentation of transparent objects can be very useful in computer vision applications. However, because they borrow texture from their background and have a similar appearance to their surroundings, transparent objects are not handled well by regular image segmentation methods. We propose a met...

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Bibliographic Details
Main Authors: Yichao Xu, Nagahara, Hajime, Shimada, Atsushi, Taniguchi, Rin-ichiro
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Computer vision
Detectors
Glass
Image segmentation
Linearity
Object segmentation
Occlusion
Pixels
Segmentation
Surface layer
Texture
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Summary:The segmentation of transparent objects can be very useful in computer vision applications. However, because they borrow texture from their background and have a similar appearance to their surroundings, transparent objects are not handled well by regular image segmentation methods. We propose a method that overcomes these problems using the consistency and distortion properties of a light-field image. Graph-cut optimization is applied for the pixel labeling problem. The light-field linearity is used to estimate the likelihood of a pixel belonging to the transparent object or Lambertian background, and the occlusion detector is used to find the occlusion boundary. We acquire a light field dataset for the transparent object, and use this dataset to evaluate our method. The results demonstrate that the proposed method successfully segments transparent objects from the background.
ISSN:2380-7504
DOI:10.1109/ICCV.2015.393