Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/48452
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dc.contributor.authorNur Shabrina Rusli.
dc.date.accessioned2012-04-24T03:18:05Z
dc.date.available2012-04-24T03:18:05Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/10356/48452
dc.description.abstractSince the Human Visual System (HVS) is the ultimate receiver and appreciator of natural scenes, many visual attention models have been developed and applied to various image processing applications in the past decade.In order to provide the insight for effective deployment in this project, we study various existing saliency detection models and different image processing techniques. The performance of the respective experiments is analyzed. Benchmarking the state-of-the-art technology is then made in the related area.en_US
dc.format.extent54 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer visionen_US
dc.titlePerceptual image processing algorithm benchmarkingen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLin Weisien_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Engineering)en_US
dc.contributor.researchCentre for Multimedia and Network Technologyen_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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