000171856 001__ 171856
000171856 005__ 20190812205550.0
000171856 020__ $$a978-1-4244-4419-9
000171856 02470 $$2ISI$$a000294955300046
000171856 037__ $$aCONF
000171856 245__ $$aCompact Signatures for High-speed Interest Point Description and Matching
000171856 269__ $$a2009
000171856 260__ $$bIeee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa$$c2009
000171856 336__ $$aConference Papers
000171856 490__ $$aIEEE International Conference on Computer Vision
000171856 520__ $$aProminent feature point descriptors such as SIFT and SURF allow reliable real-time matching but at a computational cost that limits the number of points that can be handled on PCs, and even more on less powerful mobile devices. A recently proposed technique that relies on statistical classification to compute signatures has the potential to be much faster but at the cost of using very large amounts of memory, which makes it impractical for implementation on low-memory devices.
000171856 700__ $$0242711$$g172803$$aCalonder, Michael
000171856 700__ $$0240235$$g149007$$aLepetit, Vincent
000171856 700__ $$0240252$$g112366$$aFua, Pascal
000171856 700__ $$aKonolige, Kurt
000171856 700__ $$aBowman, James
000171856 700__ $$aMihelich, Patrick
000171856 7112_ $$dSep 29-Oct 02, 2009$$cKyoto, JAPAN$$aIEEE International Conference on Computer Vision
000171856 773__ $$t2009 Ieee 12Th International Conference On Computer Vision (Iccv)$$q357-364
000171856 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/171856/files/top.pdf$$s454910
000171856 909C0 $$xU10659$$pCVLAB$$0252087
000171856 909CO $$ooai:infoscience.tind.io:171856$$qGLOBAL_SET$$pconf$$pIC
000171856 917Z8 $$x112366
000171856 937__ $$aEPFL-CONF-171856
000171856 973__ $$rNON-REVIEWED$$sPUBLISHED$$aEPFL
000171856 980__ $$aCONF