@article{
 title = {Rotation-invariant texture classification using feature distributions.},
 type = {article},
 year = {2000},
 id = {f41df741-4664-30b3-ba07-52952c327a8f},
 created = {2019-11-19T16:28:43.169Z},
 file_attached = {false},
 profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
 group_id = {28b2996c-b80f-3c26-be71-695caf7040ac},
 last_modified = {2019-11-19T16:32:41.272Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {mvg:7},
 source_type = {article},
 notes = {Pattern Recognition 33:43 - 52.},
 folder_uuids = {8292f5ec-1c57-4113-a303-25778e695f8c},
 private_publication = {false},
 abstract = {A distribution-based classification approach and a set of recently developed texture measures are applied to rotation-invariant texture classification. The performance is compared to that obtained with the well-known circular-symmetric autoregressive random field (CSAR) model approach. A difficult classification problem of 15 different Brodatz textures and seven rotation angles is used in e x periments. The results show much better performance for our approach than for the CSAR features. A detailed analysis of the confusion matrices and the rotation angles of misclassified samples produces several interesting observations about the classificat ion problem and the features used in this study.},
 bibtype = {article},
 author = {Pietikäinen, M and Ojala, T and Xu, Z}
}