2021
Multi-object tracking with deep learning ensemble for unmanned aerial system applications
Xie W, Ide J, Izadi D, Banger S, Walker T, Ceresani R, Spagnuolo D, Guagliano C, Diaz H, Twedt J. Multi-object tracking with deep learning ensemble for unmanned aerial system applications. Proceedings Of SPIE--the International Society For Optical Engineering 2021, 11870: 118700i-118700i-13. DOI: 10.1117/12.2600209.Peer-Reviewed Original ResearchMulti-object trackingUnmanned aerial systemsConvolutional neural network (CNN) encoderUnmanned Aerial System (UAS) applicationsNeural network encoderMultiple hypothesis tracking (MHT) frameworkObject tracking modelReal-time situationsDifferent similarity measuresImage embeddingEntity trajectoriesDeep learningSiamese networkNetwork encoderObject trajectoriesZoom levelAttention mechanismDynamic backgroundObject detectorTracking frameworkMOT methodsIllumination changesLatent spaceSituational awarenessSimilarity measure
2017
Classification of Cocaine Dependents from fMRI Data Using Cluster-Based Stratification and Deep Learning
Santos J, Savii R, Ide J, Li C, Quiles M, Basgalupp M. Classification of Cocaine Dependents from fMRI Data Using Cluster-Based Stratification and Deep Learning. Lecture Notes In Computer Science 2017, 10404: 298-313. DOI: 10.1007/978-3-319-62392-4_22.Peer-Reviewed Original ResearchDeep learningDeep learning methodsDeep neural networksDeep belief networkSmall data setsComputational visionClassification of pathologiesBelief networkFMRI classificationVoice recognitionNeural networkLearning methodsRobust trainingBrain decodingSmall dataData setsLearningCocaine dependenceNovel stratification methodTraditional techniquesNetworkClassificationCocaine dependentsNon-addicted individualsDrug use