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Real-time coarse-to-fine topologically preserving segmentation | IEEE Conference Publication | IEEE Xplore
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Real-time coarse-to-fine topologically preserving segmentation


Abstract:

In this paper, we tackle the problem of unsupervised segmentation in the form of superpixels. Our main emphasis is on speed and accuracy. We build on [31] to define the p...Show More

Abstract:

In this paper, we tackle the problem of unsupervised segmentation in the form of superpixels. Our main emphasis is on speed and accuracy. We build on [31] to define the problem as a boundary and topology preserving Markov random field. We propose a coarse to fine optimization technique that speeds up inference in terms of the number of updates by an order of magnitude. Our approach is shown to outperform [31] while employing a single iteration. We evaluate and compare our approach to state-of-the-art superpixel algorithms on the BSD and KITTI benchmarks. Our approach significantly outperforms the baselines in the segmentation metrics and achieves the lowest error on the stereo task.
Date of Conference: 07-12 June 2015
Date Added to IEEE Xplore: 15 October 2015
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ISSN Information:

Conference Location: Boston, MA, USA

References

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