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Segmenting â€œsimpleâ€ objects using low-level visual cues is an important capability for a vision system to learn in an unsupervised manner. We define a â€œsimpleâ€ object as a compact region enclosed by depth and/or contact boundary in the scene. We propose a segmentation process to extract all the â€œsimpleâ€ objects that builds on the fixation-based segmentation framework  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 â€œsimpleâ€ objects and a postsegmentation process to select only the regions corresponding to the â€œsimpleâ€ 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 RGB-D dataset  and finds that it successfully extracts 91.4% of all objects in the scene.
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