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A model-free approach for the segmentation of unknown objects | IEEE Conference Publication | IEEE Xplore

A model-free approach for the segmentation of unknown objects


Abstract:

We address the problem of object segmentation from depth images of highly complex indoor scenes. We propose a model-free segmentation approach, which robustly separates u...Show More

Abstract:

We address the problem of object segmentation from depth images of highly complex indoor scenes. We propose a model-free segmentation approach, which robustly separates unknown stacked objects in real-world scenes. Our approach constructs geometrically constrained 3D clusters known as salient-regions, which are subsequently merged into high-level object hypotheses by analyzing the local geometrical characteristics (such as local shape and homogeneity) of the area of their shared boundaries. We tested our approach using depth images from live Kinect video streams and publicly available RGB-D datasets. Our approach is highly efficient and achieves superior performance compared to state-of-the-art techniques.
Date of Conference: 14-18 September 2014
Date Added to IEEE Xplore: 06 November 2014
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Conference Location: Chicago, IL, USA

References

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