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融合显著与深度信息的缝切割重定向方法
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  • 英文篇名:Image Retargeting with Seam-Carving Combining Saliency and Depth Information
  • 作者:吴加莹 ; 杨赛 ; 堵俊 ; 董宁
  • 英文作者:WU Jia-ying;YANG Sai;DU Jun;DONG Ning;School of Electrical Engineering,Nantong University;Nantong Research Institute for Advanced Communication Technologies;
  • 关键词:显著信息 ; 深度信息 ; 缝切割 ; 图像重定向
  • 英文关键词:saliency information;;depth information;;seam carving;;image retargeting
  • 中文刊名:DZXU
  • 英文刊名:Acta Electronica Sinica
  • 机构:南通大学电气工程学院;南通先进通信技术研究院;
  • 出版日期:2019-07-15
  • 出版单位:电子学报
  • 年:2019
  • 期:v.47;No.437
  • 基金:国家自然科学基金(No.61602150);; 江苏省普通高校自然科学基金(No.16KJB520037);; 南通大学-南通智能信息技术联合研究中心(No.KFKT2017A02)
  • 语种:中文;
  • 页:DZXU201907020
  • 页数:4
  • CN:07
  • ISSN:11-2087/TN
  • 分类号:157-160
摘要
针对当前内容感知的重定向方法中可能出现的变形和失真问题,提出一种融合显著与深度信息的缝切割重定向方法.首先利用GBVS算法获取图像显著信息,结合图像梯度信息与通过SIFT匹配方法获取的图像深度信息构建更精确的重要度图;其次,根据重要度图的能量分布,对原始图像进行处理,得到最终的重定向结果.基于公开数据库在两个不同评价标准下与多种重定向方法的对比表明,本文方法能够最大程度的保留图像的显著部分.
        To solve the problem of deformation and distortion that may occur in the current content-aware redirection method,a retargeting method with seam-carving combining saliency and depth information is proposed.Specifically,the GBVS algorithm is used to obtain the saliency information,and the image gradient information is combined with the image depth information acquired by the SIFT matching method to construct a more accurate importance map.Then,according to the energy distribution of the importance map,the original image is processed to obtain the final results.The comparison on the public database with different methods under two evaluation criteria shows that the proposed method can preserve the significant part of the image to the greatest extent.
引文
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