  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.
