1 research outputs found
Detachable Object Detection: Segmentation and Depth Ordering From Short-Baseline Video
We describe an approach for segmenting an image into regions that correspond
to surfaces in the scene that are partially surrounded by the medium. It
integrates both appearance and motion statistics into a cost functional, that
is seeded with occluded regions and minimized efficiently by solving a linear
programming problem. Where a short observation time is insufficient to
determine whether the object is detachable, the results of the minimization can
be used to seed a more costly optimization based on a longer sequence of video
data. The result is an entirely unsupervised scheme to detect and segment an
arbitrary and unknown number of objects. We test our scheme to highlight the
potential, as well as limitations, of our approach