<?xml version="1.0" encoding="UTF-8"?>
<collection xmlns="http://www.loc.gov/MARC21/slim">
 <record>
  <leader>     caa a22        4500</leader>
  <controlfield tag="001">445294892</controlfield>
  <controlfield tag="003">CHVBK</controlfield>
  <controlfield tag="005">20180317142538.0</controlfield>
  <controlfield tag="007">cr unu---uuuuu</controlfield>
  <controlfield tag="008">170323e20100901xx      s     000 0 eng  </controlfield>
  <datafield tag="024" ind1="7" ind2="0">
   <subfield code="a">10.1007/s11571-010-9122-0</subfield>
   <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="035" ind1=" " ind2=" ">
   <subfield code="a">(NATIONALLICENCE)springer-10.1007/s11571-010-9122-0</subfield>
  </datafield>
  <datafield tag="245" ind1="0" ind2="0">
   <subfield code="a">Visual saliency: a biologically plausible contourlet-like frequency domain approach</subfield>
   <subfield code="h">[Elektronische Daten]</subfield>
   <subfield code="c">[Peng Bian, Liming Zhang]</subfield>
  </datafield>
  <datafield tag="520" ind1="3" ind2=" ">
   <subfield code="a">In this paper we propose a fast frequency domain saliency detection method that is also biologically plausible, referred to as frequency domain divisive normalization (FDN). We show that the initial feature extraction stage, common to all spatial domain approaches, can be simplified to a Fourier transform with a contourlet-like grouping of coefficients, and saliency detection can be achieved in frequency domain. Specifically, we show that divisive normalization, a model of cortical surround inhibition, can be conducted in frequency domain. Since Fourier coefficients are global in space, we extend to this model by conducting piecewise FDN (PFDN) using overlapping local patches to provide better biological plausibility. Not only do FDN and PFDN outperform current state-of-the-art methods in eye fixation prediction, they are also faster. Speed and simplicity are advantages of our frequency domain approach, and its biological plausibility is the main contribution of our paper.</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
   <subfield code="a">Springer Science+Business Media B.V., 2010</subfield>
  </datafield>
  <datafield tag="690" ind1=" " ind2="7">
   <subfield code="a">Visual saliency</subfield>
   <subfield code="2">nationallicence</subfield>
  </datafield>
  <datafield tag="690" ind1=" " ind2="7">
   <subfield code="a">Attention selection</subfield>
   <subfield code="2">nationallicence</subfield>
  </datafield>
  <datafield tag="690" ind1=" " ind2="7">
   <subfield code="a">Saliency map</subfield>
   <subfield code="2">nationallicence</subfield>
  </datafield>
  <datafield tag="690" ind1=" " ind2="7">
   <subfield code="a">Divisive normalization</subfield>
   <subfield code="2">nationallicence</subfield>
  </datafield>
  <datafield tag="700" ind1="1" ind2=" ">
   <subfield code="a">Bian</subfield>
   <subfield code="D">Peng</subfield>
   <subfield code="u">Department of Electronic Engineering, Fudan University, 200433, Shanghai, China</subfield>
   <subfield code="4">aut</subfield>
  </datafield>
  <datafield tag="700" ind1="1" ind2=" ">
   <subfield code="a">Zhang</subfield>
   <subfield code="D">Liming</subfield>
   <subfield code="u">Department of Electronic Engineering, Fudan University, 200433, Shanghai, China</subfield>
   <subfield code="4">aut</subfield>
  </datafield>
  <datafield tag="773" ind1="0" ind2=" ">
   <subfield code="t">Cognitive Neurodynamics</subfield>
   <subfield code="d">Springer Netherlands</subfield>
   <subfield code="g">4/3(2010-09-01), 189-198</subfield>
   <subfield code="x">1871-4080</subfield>
   <subfield code="q">4:3&lt;189</subfield>
   <subfield code="1">2010</subfield>
   <subfield code="2">4</subfield>
   <subfield code="o">11571</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2="0">
   <subfield code="u">https://doi.org/10.1007/s11571-010-9122-0</subfield>
   <subfield code="q">text/html</subfield>
   <subfield code="z">Onlinezugriff via DOI</subfield>
  </datafield>
  <datafield tag="908" ind1=" " ind2=" ">
   <subfield code="D">1</subfield>
   <subfield code="a">research-article</subfield>
   <subfield code="2">jats</subfield>
  </datafield>
  <datafield tag="950" ind1=" " ind2=" ">
   <subfield code="B">NATIONALLICENCE</subfield>
   <subfield code="P">856</subfield>
   <subfield code="E">40</subfield>
   <subfield code="u">https://doi.org/10.1007/s11571-010-9122-0</subfield>
   <subfield code="q">text/html</subfield>
   <subfield code="z">Onlinezugriff via DOI</subfield>
  </datafield>
  <datafield tag="950" ind1=" " ind2=" ">
   <subfield code="B">NATIONALLICENCE</subfield>
   <subfield code="P">700</subfield>
   <subfield code="E">1-</subfield>
   <subfield code="a">Bian</subfield>
   <subfield code="D">Peng</subfield>
   <subfield code="u">Department of Electronic Engineering, Fudan University, 200433, Shanghai, China</subfield>
   <subfield code="4">aut</subfield>
  </datafield>
  <datafield tag="950" ind1=" " ind2=" ">
   <subfield code="B">NATIONALLICENCE</subfield>
   <subfield code="P">700</subfield>
   <subfield code="E">1-</subfield>
   <subfield code="a">Zhang</subfield>
   <subfield code="D">Liming</subfield>
   <subfield code="u">Department of Electronic Engineering, Fudan University, 200433, Shanghai, China</subfield>
   <subfield code="4">aut</subfield>
  </datafield>
  <datafield tag="950" ind1=" " ind2=" ">
   <subfield code="B">NATIONALLICENCE</subfield>
   <subfield code="P">773</subfield>
   <subfield code="E">0-</subfield>
   <subfield code="t">Cognitive Neurodynamics</subfield>
   <subfield code="d">Springer Netherlands</subfield>
   <subfield code="g">4/3(2010-09-01), 189-198</subfield>
   <subfield code="x">1871-4080</subfield>
   <subfield code="q">4:3&lt;189</subfield>
   <subfield code="1">2010</subfield>
   <subfield code="2">4</subfield>
   <subfield code="o">11571</subfield>
  </datafield>
  <datafield tag="900" ind1=" " ind2="7">
   <subfield code="a">Metadata rights reserved</subfield>
   <subfield code="b">Springer special CC-BY-NC licence</subfield>
   <subfield code="2">nationallicence</subfield>
  </datafield>
  <datafield tag="898" ind1=" " ind2=" ">
   <subfield code="a">BK010053</subfield>
   <subfield code="b">XK010053</subfield>
   <subfield code="c">XK010000</subfield>
  </datafield>
  <datafield tag="949" ind1=" " ind2=" ">
   <subfield code="B">NATIONALLICENCE</subfield>
   <subfield code="F">NATIONALLICENCE</subfield>
   <subfield code="b">NL-springer</subfield>
  </datafield>
 </record>
</collection>
