000160453 001__ 160453
000160453 005__ 20190316235002.0
000160453 02470 $$2ISI$$a000260656000005
000160453 037__ $$aCONF
000160453 245__ $$aKeypoint Signatures for Fast Learning and Recognition
000160453 269__ $$a2008
000160453 260__ $$bSpringer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa$$c2008
000160453 336__ $$aConference Papers
000160453 490__ $$aLecture Notes In Computer Science
000160453 520__ $$aStatistical learning techniques have been used to dramatically speed-up keypoint matching by training a classifier to recognize a specific set of keypoints. However, the training itself is usually relatively slow and performed offline. Although methods have recently been proposed to train the classifier online, they can only learn a very limited number of new keypoints. This represents a handicap for real-time applications, such as Simultaneous Localization and Mapping (SLAM), which require incremental addition of arbitrary numbers of keypoints as they become visible.
000160453 6531_ $$aRandomized Trees
000160453 700__ $$0242711$$aCalonder, Michael$$g172803
000160453 700__ $$0240235$$aLepetit, Vincent$$g149007
000160453 700__ $$0240252$$aFua, Pascal$$g112366
000160453 7112_ $$aEuropean Conference on Computer Vision $$cMarseille, FRANCE$$dOct 12-18, 2008
000160453 773__ $$j5302$$q58-71$$tComputer Vision - Eccv 2008, Pt I, Proceedings
000160453 8564_ $$s1970856$$uhttps://infoscience.epfl.ch/record/160453/files/top.pdf$$yn/a$$zn/a
000160453 909C0 $$0252087$$pCVLAB$$xU10659
000160453 909CO $$ooai:infoscience.tind.io:160453$$pconf$$pIC$$qGLOBAL_SET
000160453 917Z8 $$xWOS-2010-11-30
000160453 917Z8 $$x112366
000160453 937__ $$aEPFL-CONF-160453
000160453 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000160453 980__ $$aCONF