@inProceedings{
 title = {Generalized affine moment invariants for object recognition.},
 type = {inProceedings},
 year = {2006},
 id = {cce1f01b-b17d-3758-b491-1095c8a89ec5},
 created = {2019-11-19T13:01:28.739Z},
 file_attached = {false},
 profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
 group_id = {17585b85-df99-3a34-98c2-c73e593397d7},
 last_modified = {2019-11-19T13:46:33.514Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {mvg:724},
 source_type = {inproceedings},
 notes = {Proc. 18th International Conference on Pattern Recognition (ICPR 2006), Hong Kong, 2: 4 p.},
 private_publication = {false},
 abstract = {This paper introduces a new way of extracting affine invariant
features from image functions. The presented approach
is based on combining affine moment invariants
(AMI) with multiscale invariants, in particular multiscale
autoconvolution (MSA) and spatial multiscale affine invariants
(SMA). Our approach includes all of these invariants
as special cases, but also makes it possible to construct new
ones. According to the performed experiments the introduced
features provide discriminating information for affine
invariant object classification, clearly outperforming standard
AMI, MSA, and SMA.},
 bibtype = {inProceedings},
 author = {Rahtu E Salo M, Heikkilä J & Flusser J}
}

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