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                <article article-type="research-article">
                <front>
                    <journal-meta>
                    <journal-id journal-id-type="publisher-id">cic</journal-id>
                    <journal-title>Color and Imaging Conference</journal-title>
                    <abbrev-journal-title>color imaging conf</abbrev-journal-title>
                    <issn pub-type="ppub">2166-9635</issn><issn pub-type="epub">2166-9635</issn>
                    <publisher>
                        <publisher-name>Society for Imaging Science and Technology</publisher-name>
                        <publisher-loc>IS&amp;T 7003 Kilworth Lane, Springfield, VA 22151 USA</publisher-loc>
                    </publisher>
                    </journal-meta>
                    <article-meta>
                    <article-id pub-id-type="doi">10.2352/CIC.2022.30.1.40</article-id>
                    <article-id pub-id-type="publisher-id">40</article-id>
                    <article-categories>
                        <subj-group>
                        <subject>Regular Article</subject>
                        </subj-group>
                    </article-categories>
                    <title-group>
                        <article-title>Weighted Geometric Mean (WGM) Method: A New Chromatic Adaptation Model</article-title>
                    </title-group><contrib-group content-type="all"><contrib contrib-type="author"><name>
                            <surname>Shen</surname>
                            <given-names>Che </given-names>
                           </name> <xref ref-type="aff" rid="aff1author1"/></contrib> <aff id="aff1author1">Rochester Institute of Technology, US</aff></contrib-group><contrib-group content-type="all"><contrib contrib-type="author"><name>
                            <surname>Fairchild</surname>
                            <given-names>Mark </given-names>
                           </name> <xref ref-type="aff" rid="aff1author2"/></contrib> <aff id="aff1author2">Rochester Institute of Technology, US</aff></contrib-group><abstract>
                    <title>Abstract</title>
                    <p>The geometric mean has been suggested to be the fundamental mathematical relationship that governs peripheral sensory adaptation. This paper proposes the WGM model, an advanced chromatic adaptation model based on a weighted geometric mean approach that can anticipate incomplete adaptation as it moves along the Planckian or Daylight locus. Compared with two other chromatic adaptation models (CAT16 and vK20), the WGM model shows more accuracy in predicting previous visual data.</p>
                    </abstract><pub-date>
                        <day>15</day>
                        <month>11</month>
                        <year>2022</year>
                        </pub-date><volume>30</volume>
                    <issue-acronym></issue-acronym>
                    <issue-title>30th Color and Imaging Conference</issue-title>
                    <issue>1</issue>
                    <fpage>231</fpage>
                    <lpage>235</lpage>
                    <permissions>
                         <copyright-statement>©2022 Society for Imaging Science and Technology</copyright-statement>
                        <copyright-year>2022</copyright-year>
                    </permissions><kwd-group><kwd>Chromatic adaptation</kwd><kwd>geometric mean</kwd></kwd-group></article-meta>
                </front>
                </article>