{"_id":"XXYSb7Ygd6tQ7pWMW","bibbaseid":"rahtue-anewaffineinvariantimagetransformbasedonridgelets-2006","authorIDs":[],"author_short":["Rahtu E, S., M., &., H., J."],"bibdata":{"title":"A new affine invariant image transform based on ridgelets","type":"inProceedings","year":"2006","id":"1ed1ad7c-6ab5-302c-b85a-13879497035e","created":"2019-11-19T13:01:18.406Z","file_attached":false,"profile_id":"bddcf02d-403b-3b06-9def-6d15cc293e20","group_id":"17585b85-df99-3a34-98c2-c73e593397d7","last_modified":"2019-11-19T13:46:35.658Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"mvg:739","source_type":"inproceedings","notes":"Proc. the 16th British Machine Vision Conference (BMVC 2006), Edinburgh, UK, 3:1039-1048.","private_publication":false,"abstract":"In this paper we present a new affine invariant image transform, based on\nridgelets. The proposed transform is directly applicable to segmented image\npatches. The new method has some similarities with the previously proposed\nMultiscale Autoconvolution, but it will offer a more general framework and\npossibilities for variations. The obtained transform coefficients can be used\nin affine invariant pattern classification, and as shown in the experiments, already\na small subset of them is enough for reliable recognition of complex\npatterns. The new method is assessed in several experiments and it is observed\nto perform well under many nonaffine distortions.","bibtype":"inProceedings","author":"Rahtu E, Salo M & Heikkilä J","bibtex":"@inProceedings{\n title = {A new affine invariant image transform based on ridgelets},\n type = {inProceedings},\n year = {2006},\n id = {1ed1ad7c-6ab5-302c-b85a-13879497035e},\n created = {2019-11-19T13:01:18.406Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n group_id = {17585b85-df99-3a34-98c2-c73e593397d7},\n last_modified = {2019-11-19T13:46:35.658Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {mvg:739},\n source_type = {inproceedings},\n notes = {Proc. the 16th British Machine Vision Conference (BMVC 2006), Edinburgh, UK, 3:1039-1048.},\n private_publication = {false},\n abstract = {In this paper we present a new affine invariant image transform, based on\nridgelets. The proposed transform is directly applicable to segmented image\npatches. The new method has some similarities with the previously proposed\nMultiscale Autoconvolution, but it will offer a more general framework and\npossibilities for variations. The obtained transform coefficients can be used\nin affine invariant pattern classification, and as shown in the experiments, already\na small subset of them is enough for reliable recognition of complex\npatterns. The new method is assessed in several experiments and it is observed\nto perform well under many nonaffine distortions.},\n bibtype = {inProceedings},\n author = {Rahtu E, Salo M & Heikkilä J}\n}","author_short":["Rahtu E, S., M., &., H., J."],"bibbaseid":"rahtue-anewaffineinvariantimagetransformbasedonridgelets-2006","role":"author","urls":{},"downloads":0},"bibtype":"inProceedings","creationDate":"2019-11-19T13:17:06.532Z","downloads":0,"keywords":[],"search_terms":["new","affine","invariant","image","transform","based","ridgelets","rahtu e"],"title":"A new affine invariant image transform based on ridgelets","year":2006}