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dc.contributor.authorGünay, Osmanen_US
dc.contributor.authorÇetin, A. Enisen_US
dc.coverage.spatialHaspolat, Turkeyen_US
dc.date.accessioned2016-02-08T12:07:47Z
dc.date.available2016-02-08T12:07:47Z
dc.date.issued2013en_US
dc.identifier.urihttp://hdl.handle.net/11693/27993
dc.descriptionDate of Conference: 24-26 April 2013en_US
dc.description.abstractIn this paper, a Compressive Sensing based feature extraction algorithm is proposed for flame detection using infrared cameras. First, bright and moving regions in videos are detected. Then the videos are divided into spatio-temporal blocks and spatial and temporal feature vectors are exctracted from these blocks. Compressive Sensing is used to exctract spatial feature vectors. Compressed measurements are obtained by multiplying the pixels in the block with the sensing matrix. A new method is also developed to generate the sensing matrix. A random vector generated according to standard Gaussian distribution is passed through a wavelet transform and the resulting matrix is used as the sensing matrix. Temporal features are obtained from the vector that is formed from the difference of mean intensity values of the frames in two neighboring blocks. Spatial feature vectors are classified using Adaboost. Temporal feature vectors are classified using hidden Markov models. To reduce the computational cost only moving and bright regions are classified and classification is performed at specified intervals instead of every frame. © 2013 IEEE.en_US
dc.language.isoTurkishen_US
dc.source.title2013 21st Signal Processing and Communications Applications Conference (SIU)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SIU.2013.6531547en_US
dc.subjectCompressive sensingen_US
dc.subjectFlame detectionen_US
dc.subjectImage processingen_US
dc.subjectInfrareden_US
dc.subjectWavelet transformen_US
dc.subjectA-wavelet transformen_US
dc.subjectCompressive sensingen_US
dc.subjectComputational costsen_US
dc.subjectFeature extraction algorithmsen_US
dc.subjectFlame detectionen_US
dc.subjectInfra-red camerasen_US
dc.subjectSpatial feature vectoren_US
dc.subjectTemporal featuresen_US
dc.subjectAdaptive boostingen_US
dc.subjectAlgorithmsen_US
dc.subjectHidden Markov modelsen_US
dc.subjectImage processingen_US
dc.subjectInfrared radiationen_US
dc.subjectSignal reconstructionen_US
dc.subjectVectorsen_US
dc.subjectWavelet transformsen_US
dc.subjectCompressed sensingen_US
dc.titleCompressive sensing based flame detection in infrared videosen_US
dc.title.alternativeKizilötesi videolarda sikiştirmali algilama ile alev tespitien_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.identifier.doi10.1109/SIU.2013.6531547en_US
dc.publisherIEEEen_US
dc.contributor.bilkentauthorÇetin, A. Enis


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