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We describe an approach for segmenting an image into regions that correspondto surfaces in the scene that are partially surrounded by the medium. Itintegrates both appearance and motion statistics into a cost functional, thatis seeded with occluded regions and minimized efficiently by solving a linearprogramming problem. Where a short observation time is insufficient todetermine whether the object is detachable, the results of the minimization canbe used to seed a more costly optimization based on a longer sequence of videodata. The result is an entirely unsupervised scheme to detect and segment anarbitrary and unknown number of objects. We test our scheme to highlight thepotential, as well as limitations, of our approach.

Detachable Object Detection: Segmentation and Depth Ordering From Short-Baseline Video