@inProceedings{
 title = {Local binary pattern descriptors for dynamic texture recognition.},
 type = {inProceedings},
 year = {2006},
 id = {23432a08-6824-376b-903d-988c87e91d9a},
 created = {2019-11-19T16:28:48.446Z},
 file_attached = {false},
 profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
 group_id = {28b2996c-b80f-3c26-be71-695caf7040ac},
 last_modified = {2019-11-19T16:32:42.327Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {mvg:721},
 source_type = {inproceedings},
 notes = {Proc. 18th International Conference on Pattern Recognition (ICPR 2006), Hong Kong, 2: 4 p.},
 folder_uuids = {8292f5ec-1c57-4113-a303-25778e695f8c},
 private_publication = {false},
 abstract = {Dynamic texture is an extension of texture to the
temporal domain. In this paper, a new method for
recognizing dynamic textures is proposed. The textures
are modeled with concatenated local binary patterns in
three orthonormal planes. The circular neighborhoods
are generalized to elliptical sampling to fit to the
space-time statistics. This is an extension of the LBP
approach widely used in still texture analysis,
combining the motion and appearance together. Our
approach has many advantages compared with the
earlier approaches providing a better performance for
the DynTex and MIT databases.},
 bibtype = {inProceedings},
 author = {Zhao, G and Pietikäinen, M}
}

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