Abstract
Recent advances on convex relaxation methods allow for a flexible formulation of many interactive multi-label segmentation methods. The building blocks are a likelihood specified for each pixel and each label, and a penalty for the boundary length of each segment. While many sophisticated likelihood estimations based on various statistical measures have been investigated, the boundary length is usually measured in a metric induced by simple image gradients. We show that complementing these methods with recent advances of edge detectors yields an immense quality improvement. A remarkable feature of the proposed method is the ability to correct some erroneous labels, when computer generated initial labels are considered. This allows us to improve state-of-the-art methods for motion segmentation in videos by 5–10 % with respect to the F-measure (Dice score).
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Notes
- 1.
Instead of integrating over the set of scribbles they sum over all scribbled pixels.
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Acknowledgements
This work was partially funded by the European Union’s FP7 under the project Computational Horizons in Cancer (grant agreement No. 600841).
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Müller, S., Ochs, P., Weickert, J., Graf, N. (2016). Robust Interactive Multi-label Segmentation with an Advanced Edge Detector. In: Rosenhahn, B., Andres, B. (eds) Pattern Recognition. GCPR 2016. Lecture Notes in Computer Science(), vol 9796. Springer, Cham. https://doi.org/10.1007/978-3-319-45886-1_10
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