An In Depth View of Saliency
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Abstract
Visual saliency is a computational process that identifies important locations and
structure in the visual field. Most current methods for saliency rely on cues such as
color and texture while ignoring depth information, which is known to be an important
saliency cue in the human cognitive system. We propose a novel computational model of
visual saliency which incorporates depth information. We compare our approach to several state of the art visual saliency methods and we introduce a method for saliency based
segmentation of generic objects. We demonstrate that by explicitly constructing 3D lay-out and shape features from depth measurements, we can obtain better performance than
methods which treat the depth map as just another image channel. Our method requires
no learning and can operate on scenes for which the system has no previous knowledge.
We conduct object segmentation experiments on a new dataset of registered RGB-D images captured on a mobile-manipulator robot.
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Date
2013-09
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Proceedings