Dynamic texture analysis in video with application to flame, smoke and volatile organic compound vapor detection

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buir.advisorÇetin, A. Enis
dc.contributor.authorGünay, Osman
dc.date.accessioned2016-01-08T18:10:42Z
dc.date.available2016-01-08T18:10:42Z
dc.date.issued2009
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2009.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2009.en_US
dc.descriptionIncludes bibliographical references leaves 74-82.en_US
dc.description.abstractDynamic textures are moving image sequences that exhibit stationary characteristics in time such as fire, smoke, volatile organic compound (VOC) plumes, waves, etc. Most surveillance applications already have motion detection and recognition capability, but dynamic texture detection algorithms are not integral part of these applications. In this thesis, image processing based algorithms for detection of specific dynamic textures are developed. Our methods can be developed in practical surveillance applications to detect VOC leaks, fire and smoke. The method developed for VOC emission detection in infrared videos uses a change detection algorithm to find the rising VOC plume. The rising characteristic of the plume is detected using a hidden Markov model (HMM). The dark regions that are formed on the leaking equipment are found using a background subtraction algorithm. Another method is developed based on an active learning algorithm that is used to detect wild fires at night and close range flames. The active learning algorithm is based on the Least-Mean-Square (LMS) method. Decisions from the sub-algorithms, each of which characterize a certain property of the texture to be detected, are combined using the LMS algorithm to reach a final decision. Another image processing method is developed to detect fire and smoke from moving camera video sequences. The global motion of the camera is compensated by finding an affine transformation between the frames using optical flow and RANSAC. Three frame change detection methods with motion compensation are used for fire detection with a moving camera. A background subtraction algorithm with global motion estimation is developed for smoke detection.en_US
dc.description.provenanceMade available in DSpace on 2016-01-08T18:10:42Z (GMT). No. of bitstreams: 1 0003852.pdf: 1490557 bytes, checksum: 4d770251cf43831d898b1a4697f01167 (MD5)en
dc.description.statementofresponsibilityGünay, Osmanen_US
dc.format.extentxiv, 82 leaves, illustrationsen_US
dc.identifier.urihttp://hdl.handle.net/11693/14901
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectVOC leak detectionen_US
dc.subjectRANSACen_US
dc.subjectmotion compensationen_US
dc.subjectoptical flowen_US
dc.subjectactive learningen_US
dc.subjectleast-meansquare (LMS) algorithmen_US
dc.subjecthidden Markov modelsen_US
dc.subjectdynamic texturesen_US
dc.subjectcomputer visionen_US
dc.subjectnight-fire detectionen_US
dc.subjectsmoke detectionen_US
dc.subjectflame detectionen_US
dc.subject.lccTA1634 .G85 2009en_US
dc.subject.lcshComputer vision.en_US
dc.subject.lcshImage processing.en_US
dc.subject.lcshVideo compression.en_US
dc.subject.lcshVisual texture recognition.en_US
dc.subject.lcshVolatile organic compounds.en_US
dc.titleDynamic texture analysis in video with application to flame, smoke and volatile organic compound vapor detectionen_US
dc.typeThesisen_US
thesis.degree.disciplineElectrical and Electronic Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

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