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<article article-type="research-article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="aggregator">72010604</journal-id>
      <journal-title>Electronic Imaging</journal-title>
      <issn pub-type="ppub">2470-1173</issn><issn pub-type="epub"></issn>
      <publisher>
        <publisher-name>Society for Imaging Science and Technology</publisher-name>
        <publisher-loc>7003 Kilworth Lane, Springfield, VA 22151 USA</publisher-loc>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.2352/ISSN.2470-1173.2016.16.HVEI-127</article-id>
      <article-id pub-id-type="sici">2470-1173(20160214)2016:16L.1;1-</article-id>
      <article-id pub-id-type="publisher-id">ei_24701173_v2016n16_input/s34.xml</article-id>
      <article-id pub-id-type="other">/ist/ei/2016/00002016/00000016/art00033</article-id>
      <article-categories>
        <subj-group>
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Using Eye Tracking Metrics and Visual Saliency Maps to Assess Image Utility</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>Matzen</surname>
            <given-names>Laura E</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Haass</surname>
            <given-names>Michael J</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Tran</surname>
            <given-names>Jonathan</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>McNamara</surname>
            <given-names>Laura A</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>14</day>
        <month>02</month>
        <year>2016</year>
      </pub-date>
      <volume>2016</volume>
      <issue>16</issue>
      <fpage>1</fpage>
      <lpage>8</lpage>
      <permissions>
        <copyright-year>2016</copyright-year>
      </permissions>
      <abstract>
        <p>
          <italic>In this study, eye tracking metrics and visual saliency maps were used to assess analysts’ interactions with synthetic aperture radar (SAR) imagery. Participants with varying levels of experience with SAR imagery completed a target detection task while their eye movements and
 behavioral responses were recorded. The resulting gaze maps were compared with maps of bottom-up visual saliency and with maps of automatically detected image features. The results showed striking differences between professional SAR analysts and novices in terms of how their visual search
 patterns related to the visual saliency of features in the imagery. They also revealed patterns that reflect the utility of various features in the images for the professional analysts. These findings have implications for system design and for the design and use of automatic feature classification
 algorithms.</italic>
        </p>
      </abstract>
    </article-meta>
  </front>
</article>
