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dc.contributor.authorHussain , Md Akmol
dc.contributor.authorSheikh-Akbari, Akbar
dc.contributor.authorMporas, Iosif
dc.date.accessioned2019-05-16T14:05:01Z
dc.date.available2019-05-16T14:05:01Z
dc.date.issued2019-05-15
dc.identifier.citationHussain , M A , Sheikh-Akbari , A & Mporas , I 2019 , ' Colour Constancy for Image of Non-Uniformly Lit Scenes ' , Sensors , vol. 19 , no. 10 , 2242 , pp. 1-19 . https://doi.org/10.3390/s19102242
dc.identifier.issn1424-8220
dc.identifier.otherPURE: 16749872
dc.identifier.otherPURE UUID: d16296aa-051d-458d-8016-30b5ed6d51df
dc.identifier.otherScopus: 85066265478
dc.identifier.urihttp://hdl.handle.net/2299/21338
dc.description.abstractDigital camera sensors are designed to record all incident light from a captured scene, but they are unable to distinguish between the colour of the light source and the true colour of objects. The resulting captured image exhibits a colour cast toward the colour of light source. This paper presents a colour constancy algorithm for images of scenes lit by non-uniform light sources. The proposed algorithm uses a histogram-based algorithm to determine the number of colour regions. It then applies the K-means++ algorithm on the input image, dividing the image into its segments. The proposed algorithm computes the Normalized Average Absolute Difference (NAAD) for each segment and uses it as a measure to determine if the segment has sufficient colour variations. The initial colour constancy adjustment factors for each segment with sufficient colour variation is calculated. The Colour Constancy Adjustment Weighting Factors (CCAWF) for each pixel of the image are determined by fusing the CCAWFs of the segments, weighted by their normalized Euclidian distance of the pixel from the center of the segments. Results show that the proposed method outperforms the statistical techniques and its images exhibit significantly higher subjective quality to those of the learning-based methods. In addition, the execution time of the proposed algorithm is comparable to statistical-based techniques and is much lower than those of the state-of-the-art learning-based methods.en
dc.format.extent19
dc.language.isoeng
dc.relation.ispartofSensors
dc.subjectCharge-coupled device sensor
dc.subjectColour constancy
dc.subjectFusion
dc.subjectK-means segmentation
dc.subjectMulti-illuminants
dc.subjectAnalytical Chemistry
dc.subjectAtomic and Molecular Physics, and Optics
dc.subjectBiochemistry
dc.subjectInstrumentation
dc.subjectElectrical and Electronic Engineering
dc.titleColour Constancy for Image of Non-Uniformly Lit Scenesen
dc.contributor.institutionSchool of Engineering and Technology
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85066265478&partnerID=8YFLogxK
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.3390/s19102242
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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