@inProceedings{
 title = {Dictionary Based Image Segmentation},
 type = {inProceedings},
 year = {2015},
 identifiers = {[object Object]},
 pages = {26-37},
 volume = {9127},
 websites = {http://link.springer.com/10.1007/978-3-319-19665-7},
 publisher = {Springer International Publishing},
 city = {Cham},
 series = {Lecture Notes in Computer Science},
 editors = {[object Object],[object Object]},
 id = {f43b719b-b80a-3ac8-8f47-4b943bf759e8},
 created = {2016-03-11T10:08:58.000Z},
 file_attached = {true},
 profile_id = {c7a58661-9ade-3ec7-833a-956e550fad26},
 group_id = {d0e361cc-e4f9-3361-a178-f7e6d1f6363f},
 last_modified = {2017-03-14T17:33:24.808Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {BjorholmDahl2015},
 private_publication = {false},
 abstract = {We propose a method for weakly supervised segmentation of natural images, which may contain both textured or non-textured regions. Our texture representation is based on a dictionary of image patches. To divide an image into separated regions with similar texture we use an implicit level sets representation of the curve, which makes our method topologically adaptive. In addition, we suggest a multi-label version of the method. Finally, we improve upon a similar texture rep-resentation, by formulating the computation of a texture probability in terms of a matrix multiplication. This results in an efficient implemen-tation of our segmentation method. We experimentally validated our approach on a number of natural as well as composed images.},
 bibtype = {inProceedings},
 author = {Bjorholm Dahl, Anders and Andersen Dahl, Vedrana},
 booktitle = {Image Analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015.}
}

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