Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/3335
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dc.contributor.advisorDagher, Issamen_US
dc.contributor.authorHseiky, Rashaen_US
dc.contributor.authorAbdallah, Widaden_US
dc.date.accessioned2020-12-23T14:35:14Z-
dc.date.available2020-12-23T14:35:14Z-
dc.date.issued2012-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/3335-
dc.descriptionIncludes bibliographical references (p.35).en_US
dc.descriptionSupervised by Dr. Issam Dagher.en_US
dc.description.abstractThis project deals with Adaptive Background Modeling from an Image Sequence using Gaussian Mixture Modeling. It analyzes its system concept and algorithm steps. The Mixture of Gaussian which is proposed by Staufer and Grimson is a popular technique for modeling adaptive background in this research and many other researches. It deals with multimodal backgrounds that are exposed to complex environmental conditions, camera shakings and illumination changes .The Gaussian Mixture method deals with the detection of moving objects by modeling pixel grey level distribution along the time based on the learning and updating of background pixel distributions. This project employs the principle of the normal distribution and the basic concept of clustering using the Gaussian Mixture. The background subtraction techniques before GMM are discussed. The system is studied using a video example to study the parameters` update and the classification of pixels. Then, the experimental results show that the GMM technique demonstrates good segmentation and proves capable of using an adaptive method to accumulate data over time for constructing the background model.en_US
dc.description.statementofresponsibilityBy Rasha Hseiky, Widad Abdallahen_US
dc.format.extentvii, 35 p. :ill. ;30 cmen_US
dc.language.isoengen_US
dc.rightsThis object is protected by copyright, and is made available here for research and educational purposes. Permission to reuse, publish, or reproduce the object beyond the personal and educational use exceptions must be obtained from the copyright holderen_US
dc.subject.lcshGaussian processesen_US
dc.titleGaussian mixture modeling for video background substractionen_US
dc.typeProjecten_US
dc.contributor.departmentDepartment of Electrical Engineeringen_US
dc.contributor.facultyFaculty of Engineeringen_US
dc.contributor.institutionUniversity of Balamanden_US
dc.date.catalogued2012-03-06-
dc.description.degreeMS in Electrical Engineeringen_US
dc.description.statusPublisheden_US
dc.identifier.ezproxyURLhttp://ezsecureaccess.balamand.edu.lb/login?url=http://olib.balamand.edu.lb/projects_and_theses/GP-EE-112.pdfen_US
dc.identifier.OlibID116787-
dc.provenance.recordsourceOliben_US
Appears in Collections:UOB Theses and Projects
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