2024
Using Computer Vision to Detect E-cigarette Content in TikTok Videos
Murthy D, Ouellette R, Anand T, Radhakrishnan S, Mohan N, Lee J, Kong G. Using Computer Vision to Detect E-cigarette Content in TikTok Videos. Nicotine & Tobacco Research 2024, 26: s36-s42. PMID: 38366342, PMCID: PMC10873490, DOI: 10.1093/ntr/ntad184.Peer-Reviewed Original ResearchConceptsE-cigarette-related contentSocial media platformsVideo-based social media platformsMedia platformsComputer vision modelsAverage F1 scoreComputer vision methodsComputer vision techniquesMachine learning modelsText-based approachComputer visionObject detectionAnnotated imagesVisual contentSocial mediaF1 scoreVision techniquesRecall valuesVision methodsVision modelsSocial media platformLearning modelsVideoComputerTikTok posts
2021
grasviq: an image analysis framework for automatically quantifying vein number and morphology in grass leaves
Robil J, Gao K, Neighbors C, Boeding M, Carland F, Bunyak F, McSteen P. grasviq: an image analysis framework for automatically quantifying vein number and morphology in grass leaves. The Plant Journal 2021, 107: 629-648. PMID: 33914380, DOI: 10.1111/tpj.15299.Peer-Reviewed Original ResearchConceptsVein traitsClassical computer vision techniquesGrass speciesComputer vision techniquesParallel venationImage data setsImage analysis frameworkProductivity of plantsVision techniquesAuxin biosynthesisMutant screensComputational image analysisInterveinal distanceGenetic experimentsVein densityOryza sativaTransport mutantsImage analysis programVein patternsLeaf piecesVein numberZea maysReticulate venationManual quantificationGrass leaves
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