TY - GEN
T1 - Object detection and recognition using structured dimensionality reduction
AU - Sharon, Ran
AU - Francos, Joseph M.
AU - Hagege, Rami R.
PY - 2013/12/1
Y1 - 2013/12/1
N2 - Assume we have a set of observations (for example, images) of different objects, each undergoing a different geometric deformation, yet all the deformations belong to the same family of deformations, Q. As a result of the action of Q, the set of different realizations for each object is generally a manifold in the space of observations. In cases where Q admits a finite dimensional representation, there is a mapping from the space of observations to a low dimensional linear subspace. Under this mapping, observations from the same manifold are mapped to the same subspace, as detailed below.
AB - Assume we have a set of observations (for example, images) of different objects, each undergoing a different geometric deformation, yet all the deformations belong to the same family of deformations, Q. As a result of the action of Q, the set of different realizations for each object is generally a manifold in the space of observations. In cases where Q admits a finite dimensional representation, there is a mapping from the space of observations to a low dimensional linear subspace. Under this mapping, observations from the same manifold are mapped to the same subspace, as detailed below.
UR - http://www.scopus.com/inward/record.url?scp=84897694079&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2013.6736957
DO - 10.1109/GlobalSIP.2013.6736957
M3 - Conference contribution
SN - 9781479902484
T3 - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
SP - 619
EP - 620
BT - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
T2 - 2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
Y2 - 3 December 2013 through 5 December 2013
ER -