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Robust interactive multi-label segmentation with an advanced edge detector S. Müller, P. Ochs, J. Weickert and N. Graf |
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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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Citation: S. Müller, P. Ochs, J. Weickert, N. Graf: Robust interactive multi-label segmentation with an advanced edge detector. ![[pdf]](/images/pdf_14x14.png) In B. Andres, B. Rosenhahn (Eds.): German Conference on Pattern Recognition (GCPR). Lecture Notes in Computer Science, Vol. 9796, 117-128, Springer, 2016. |
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Bibtex: @inproceedings{MOWG16, title = {Robust interactive multi-label segmentation with an advanced edge detector}, author = {S. M{\"u}ller and P. Ochs and J. Weickert and N. Graf}, year = {2016}, editor = {B. Andres and B. Rosenhahn}, booktitle = {German Conference on Pattern Recognition (GCPR)}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, volume = {9796}, pages = {117--128} } |
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