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Perception.html
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<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Video or Image Perception</title>
<link rel="stylesheet" href="../css/normal.css" />
<script type="text/javascript" src="../js/head2.js"></script>
<script src="../js/jQueryAssets/jquery-1.11.1.min.js"></script>
<script src="../js/jQueryAssets/jquery.ui-1.10.4.button.min.js"></script>
<link href="../js/jQueryAssets/jquery.ui.core.min.css" rel="stylesheet" type="text/css">
<link href="../js/jQueryAssets/jquery.ui.theme.min.css" rel="stylesheet" type="text/css">
<link href="../js/jQueryAssets/jquery.ui.button.min.css" rel="stylesheet" type="text/css">
</head>
<body>
<div class="banner" style="color:white; text-align: center;font-size: 45px;font-weight: bold;">
Research
</div>
<h1>Video or Image Perception</h1>
<div class="mainbody">
<img src="../img/saliency.jpg" style="text-align: center; margin:20px auto;width:100%;"></img>
<p class="brief">
Saliency prediction is a computer vision task, in order to predict the human attention when viewing images or videos. Here, we utilize machine learning methods, such as sparse coding, support vector mechanism and deep natural network to generate pixel-wise saliency map in various situations.
</p>
<h2>Related Publications</h2>
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<div align="right">
Keyword search(Case Insensitive):
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</div>
<hr/>
<ul class="pubul" id="show_news">
</ul>
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</body>
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</html>
<script src="../js/perception.js" type="text/javascript" charset="utf-8"></script>