Segmenting “simple” objects using RGB-D

Segmenting “simple” objects using low-level visual cues is an important capability for a vision system to learn in an unsupervised manner. We define a “simple” object as a compact region enclosed by depth and/or contact boundary in the scene. We propose a segmentation process to extract all the “simple” objects that builds on the fixation-based segmentation framework [1] that segments a region given a point anywhere inside it. In this work, we augment that framework with a fixation strategy to automatically select points inside the “simple” objects and a post-segmentation process to select only the regions corresponding to the “simple” objects in the scene. A novel characteristic of our approach is the incorporation of border ownership, the knowledge about the object side of a boundary pixel. We evaluate the process on a publicly available RGB-D dataset [2] and find that the proposed method successfully extracts 91.4% of all objects in the dataset.