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A weighted KNN epipolar geometry-based approach for vision-based indoor localization using smartphone cameras

contributor authorSadeghi, H. , Valaee, S. , Shirani, S.
date accessioned2020-03-12T20:39:41Z
date available2020-03-12T20:39:41Z
date issued2014
identifier other6882332.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/1021031?locale-attribute=en&show=full
formatgeneral
languageEnglish
publisherIEEE
titleA weighted KNN epipolar geometry-based approach for vision-based indoor localization using smartphone cameras
typeConference Paper
contenttypeMetadata Only
identifier padid8145623
subject keywordsoptimisation
subject keywordsquantisation (signal)
subject keywordstime-varying networks
subject keywordsdistributed model
subject keywordsdistributed saddle-point optimization
subject keywordsiteration
subject keywordsprobabilistically quantized information
subject keywordstime-varying network
subject keywordstime-varying networks
subject keywordsweighted averaging
subject keywordsConvergence
subject keywordsEducational institutions
subject keywordsLinear programming
subject keywordsOptimization
subject keywordsProbabilistic logic
subject keywordsQuantization (signal)
subject keywordsVectors
subject keywordsdistributed consensus
subject keywordsdistributed optimization
subject keywordsmulti-agent systems
subject keywordsprobabilistic quantization
identifier doi10.1109/ICOSP.2014.7015388
journal titleensor Array and Multichannel Signal Processing Workshop (SAM), 2014 IEEE 8th
filesize333474
citations0


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