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dc.contributor.authorNarayanaswamy, Manjula
dc.contributor.authorZhao, Yafan
dc.contributor.authorFung, Wai Keung
dc.contributor.authorFough, Nazila
dc.date.accessioned2021-02-25T12:02:44Z
dc.date.available2021-02-25T12:02:44Z
dc.date.issued2020-12-28
dc.identifier.citationM. Narayanaswamy, Y. Zhao, W. K. Fung and N. Fough (2020) "A Low-complexity Wavelet-based Visual Saliency Model to Predict Fixations," 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Glasgow, Scotland, UK, 2020, pp. 1-4, doi: 10.1109/ICECS49266.2020.9294905.en_US
dc.identifier.isbn978-1-7281-6044-3
dc.identifier.urihttp://hdl.handle.net/10369/11326
dc.descriptionConference paper published in proceedings of 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS) available at https://doi.org/10.1109/ICECS49266.2020.9294905en_US
dc.description.abstractA low-complexity wavelet-based visual saliency model to predict the regions of human eye fixations in images using low-level features is proposed. Unlike the existing wavelet-based saliency detection models, the proposed model requires only two channels - luminance (Y) and chrominance (Cr) in YCbCr colour space for saliency computation. These two channels are decomposed to their lowest resolution using Discrete Wavelet Transform (DWT) to extract local contrast features at multiple scales. These features are integrated at multiple levels using 2D entropy based combination scheme to derive a combined map. The combined map is normalised and enhanced using natural logarithm transformation to derive a final saliency map. The experimental results show that the proposed model has achieved better prediction accuracy with significant complexity reduction compared to the existing benchmark models over two large public image datasets.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS 2020);
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectComputational Modellingen_US
dc.subjectMathematical Modelen_US
dc.subjectPredictive Modelen_US
dc.subjectImage Colour Analysisen_US
dc.subjectVisual Saliencyen_US
dc.subjectDiscrete Wavelet Transformen_US
dc.titleA Low-complexity Wavelet-based Visual Saliency Model to Predict Fixationsen_US
dc.typeConference paperen_US
dc.identifier.doihttps://doi.org/10.1109/ICECS49266.2020.9294905
dcterms.dateAccepted2020
rioxxterms.funderCardiff Metropolitan Universityen_US
rioxxterms.identifier.projectCardiff Metropolian (Internal)en_US
rioxxterms.versionAMen_US
rioxxterms.freetoread.startdate2022-12-28
rioxxterms.funder.project37baf166-7129-4cd4-b6a1-507454d1372een_US


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