Abstract:
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This paper proposes a system that relates objects
in an image using occlusion cues and arranges them according
to depth. The system does not rely on a priori knowledge of
the scene structure and focuses on detecting special points,
such as T-junctions and highly convex contours, to infer the
depth relationships between objects in the scene. The system
makes extensive use of the binary partition tree as hierarchical
region-based image representation jointly with a new approach
for candidate T-junction estimation. Since some regions may
not involve T-junctions, occlusion is also detected by examining
convex shapes on region boundaries. Combining T-junctions and
convexity leads to a system which only relies on low level depth
cues and does not rely on semantic information. However, it
shows a similar or better performance with the state-of-the-art
while not assuming any type of scene.
As an extension of the automatic depth ordering system, a
semi-automatic approach is also proposed. If the user provides
the depth order for a subset of regions in the image, the system
is able to easily integrate this user information to the final
depth order for the complete image. For some applications, user
interaction can naturally be integrated, improving the quality of
the automatically generated depth map. |