000124932 001__ 124932
000124932 005__ 20190205040437.0
000124932 02470 $$2ISI$$a000286091100014
000124932 037__ $$aCONF
000124932 245__ $$aMobile museum guide based on fast SIFT recognition
000124932 260__ $$bSpringer$$c2008
000124932 269__ $$a2008
000124932 336__ $$aConference Papers
000124932 490__ $$aLecture Notes in Computer Science
000124932 520__ $$aThis article explores the feasibility of a market-ready, mo- bile pattern recognition system based on the latest findings in the field of object recognition and currently available hardware and network technology. More precisely, an innovative, mobile museum guide system is presented, which enables camera phones to recognize paintings in art galleries. After careful examination, the algorithms Scale- Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) were found most promising for this goal. Consequently, both have been integrated in a fully implemented prototype system and their performance has been thoroughly evaluated under realistic conditions. In order to speed up the matching process for finding the corresponding sample in the feature database, an approximation to Nearest Neighbor Search was investigated. The k-means based clustering approach was found to significantly improve the computational time.
000124932 6531_ $$aLTS4
000124932 6531_ $$amobile pattern recognition
000124932 700__ $$aRuf, Boris
000124932 700__ $$0240462$$aKokiopoulou, Effrosyni$$g170201
000124932 700__ $$aDetyniecki, Marcin
000124932 7112_ $$a6th International Workshop on Adaptive Multimedia Retrieval$$cBerlin, Germany$$dJune 26-27, 2008
000124932 773__ $$t6th International Workshop on Adaptive Multimedia Retrieval
000124932 8564_ $$zURL
000124932 8564_ $$s554503$$uhttps://infoscience.epfl.ch/record/124932/files/AMR2008.pdf$$zn/a
000124932 909C0 $$0252393$$pLTS4$$xU10851
000124932 909CO $$ooai:infoscience.tind.io:124932$$pconf$$pSTI$$qGLOBAL_SET
000124932 937__ $$aEPFL-CONF-124932
000124932 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000124932 980__ $$aCONF