Abstract:
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In this paper, we propose a system to obtain a depth ordered seg-
mentation of a single image based on low level cues. The algorithm
first constructs a hierarchical, region-based image representation of
the image using a Binary Partition Tree (BPT). During the building
process, T-junction depth cues are detected, along with high convex
boundaries. When the BPT is built, a suitable segmentation is found
and a global depth ordering is found using a probabilistic framework.
Results are compared with state of the art depth ordering and
figure/ground labeling systems. The advantage of the proposed ap-
proach compared to systems based on a training procedure is the
lack of assumptions about the scene content. Moreover, it is shown
that the system outperforms previously low-level cue based systems,
while offering similar results to a priori trained figure/ground label-
ing algorithms |