@inProceedings{
 title = {Texture classification by multi-predicate Local Binary Pattern operators.},
 type = {inProceedings},
 year = {2000},
 id = {ec2ddb99-5860-35c7-8f63-9112e628e861},
 created = {2019-11-19T16:28:47.588Z},
 file_attached = {false},
 profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
 group_id = {28b2996c-b80f-3c26-be71-695caf7040ac},
 last_modified = {2019-11-19T16:30:26.632Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {mvg:1},
 source_type = {inproceedings},
 notes = {Proc. 15th International Conference on Pattern Recognition, Barcelona, Spain, 3:951 - 954.},
 folder_uuids = {8292f5ec-1c57-4113-a303-25778e695f8c},
 private_publication = {false},
 abstract = {This paper benchmarks the simple Local Binary Pattern (LBP) approach in the supervised texture segmentation problems of the recent comparative study of Randen and Husøy. A multi-predicate version of LBP is proposed, making our approach even more powerful for images containing textures at multiple scales.},
 bibtype = {inProceedings},
 author = {Mäenpää, T and Pietikäinen, M and Ojala, T}
}

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