Improving object extraction with depth-based methods

Abstract

In this work, we introduce a method to do object extraction in RGBD images. Our method consists in a depth-based approach which provides an insight into connectedness, proximity and planarity of the scene. We combine the depth and the color in a GraphCut framework to achieve robustness. Specifically, we propose a depth-based seeding which reduces the uncertainty and limitations of the traditional color based seeding. The results of our depth-based seeding were satisfactory and allowed good segmentation results at indoor environments. An extension of our method to do video segmentation using contour graphs is also discussed.