<efrbr:recordSet xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:efrbr="http://vfrbr.info/efrbr/1.1" xmlns:efrbr-work="http://vfrbr.info/efrbr/1.1/work" xmlns:efrbr-expression="http://vfrbr.info/efrbr/1.1/expression" xmlns:efrbr-manifestation="http://vfrbr.info/efrbr/1.1/manifestation" xmlns:efrbr-person="http://vfrbr.info/efrbr/1.1/person" xmlns:efrbr-corporateBody="http://vfrbr.info/efrbr/1.1/corporateBody" xmlns:efrbr-concept="http://vfrbr.info/efrbr/1.1/concept" xmlns:efrbr-structure="http://vfrbr.info/efrbr/1.1/structure" xmlns:efrbr-responsible="http://vfrbr.info/efrbr/1.1/responsible" xmlns:efrbr-subject="http://vfrbr.info/efrbr/1.1/subject" xmlns:efrbr-other="http://vfrbr.info/efrbr/1.1/other" xsi:schemaLocation="http://vfrbr.info/efrbr/1.1 http://vfrbr.info/schemas/1.1/efrbr.xsd"><efrbr:entities><efrbr-work:work identifier="http://purl.tuc.gr/dl/dias/DB6996CE-C724-4B6F-94FC-645EF0768387"><efrbr-work:titleOfTheWork>High level saliency prediction for smart game balancing</efrbr-work:titleOfTheWork></efrbr-work:work><efrbr-expression:expression identifier="http://purl.tuc.gr/dl/dias/DB6996CE-C724-4B6F-94FC-645EF0768387"><efrbr-expression:titleOfTheExpression>High level saliency prediction for smart game balancing</efrbr-expression:titleOfTheExpression><efrbr-expression:formOfExpression vocabulary="DIAS:TYPES">
            Πλήρης Δημοσίευση σε Συνέδριο
            Conference Full Paper
         </efrbr-expression:formOfExpression><efrbr-expression:dateOfExpression type="issued">2015-11-02</efrbr-expression:dateOfExpression><efrbr-expression:dateOfExpression type="published">2014</efrbr-expression:dateOfExpression><efrbr-expression:languageOfExpression vocabulary="iso639-1">en</efrbr-expression:languageOfExpression><efrbr-expression:summarizationOfContent>Predicting visual attention can significantly improve scene design, interactivity and 
rendering. For example, image synthesis can be accelerated by reducing computation on 
non-attended scene regions; attention can also be used to improve LOD. Most previous 
attention models are based on low-level image features, as it is computationally and 
conceptually challenging to take into account highlevel factors such as scene context, 
topology or task.</efrbr-expression:summarizationOfContent><efrbr-expression:useRestrictionsOnTheExpression type="creative-commons">http://creativecommons.org/licenses/by/4.0/</efrbr-expression:useRestrictionsOnTheExpression><efrbr-expression:note type="page range">73-73</efrbr-expression:note><efrbr-expression:note type="conference name">ACM SIGGRAPH 2014 Talks</efrbr-expression:note></efrbr-expression:expression><efrbr-person:person identifier="http://users.isc.tuc.gr/~amania"><efrbr-person:nameOfPerson vocabulary="TUC:LDAP">
            Mania Aikaterini
            Μανια Αικατερινη
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            George Alex Koulieris
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            George Drettakis
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            Douglas Cunningham
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            Association for Computing Machinery
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