@inbook{
 type = {inbook},
 year = {2011},
 pages = {48-58},
 volume = {6688 LNCS},
 websites = {http://link.springer.com/10.1007/978-3-642-21227-7_5},
 publisher = {Springer, Berlin, Heidelberg},
 city = {Ystad Saltsjöbad, SW},
 id = {abb321a9-c721-3ed1-a14c-f9808cc8a732},
 created = {2015-02-18T08:30:19.000Z},
 file_attached = {false},
 profile_id = {53d1e3c7-2f16-3c81-9a84-dccd45be4841},
 last_modified = {2019-11-08T01:40:25.406Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Donoser2011},
 notes = {Oral},
 folder_uuids = {0ec41d70-75f1-4a99-820b-0a83ccc37f54},
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 abstract = {This paper deals with segmentation of image sequences in an unsupervised manner with the goal of getting highly consistent segmentation results from frame-to-frame. We first introduce a segmentation method that uses results of the previous frame as initialization and significantly improves consistency in comparison to a single frame based approach. We also find correspondences between the segmented regions from one frame to the next to further increase consistency. This matching step is based on a modified version of an efficient partial shape matching method which allows identification of similar parts of regions despite topology changes like merges and splits. We use the identified matched parts to define a partial matching cost which is then used as input to pairwise graph matching. Experiments demonstrate that we can achieve highly consistent segmentations for diverse image sequences, even allowing to track manually initialized moving and static objects. © 2011 Springer-Verlag.},
 bibtype = {inbook},
 author = {Donoser, Michael and Urschler, Martin and Riemenschneider, Hayko and Bischof, Horst},
 editor = {Heyden, A. and Kahl, F.},
 doi = {10.1007/978-3-642-21227-7_5},
 chapter = {Highly Consistent Sequential Segmentation},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

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