TransCut: Transparent Object Segmentation from a Light-Field Image
This addresses a specific challenge in computer vision for applications requiring accurate object segmentation, but it is incremental as it builds on existing methods like graph-cut optimization.
The paper tackles the problem of segmenting transparent objects in computer vision by leveraging light-field image properties, achieving successful segmentation from the background as demonstrated on a newly acquired dataset.
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.