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陈卓, 刘艳丽, 杨红雨. 单幅室外图像的高阶能量方程阴影检测算法[J]. 计算机辅助设计与图形学学报, 2019, 31(7): 1102-1109. DOI: 10.3724/SP.J.1089.2019.17388
引用本文: 陈卓, 刘艳丽, 杨红雨. 单幅室外图像的高阶能量方程阴影检测算法[J]. 计算机辅助设计与图形学学报, 2019, 31(7): 1102-1109. DOI: 10.3724/SP.J.1089.2019.17388
Chen Zhuo, Liu Yanli, Yang Hongyu. Detecting Shadows from a Single Outdoor Image Based on High Order Energy Function[J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(7): 1102-1109. DOI: 10.3724/SP.J.1089.2019.17388
Citation: Chen Zhuo, Liu Yanli, Yang Hongyu. Detecting Shadows from a Single Outdoor Image Based on High Order Energy Function[J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(7): 1102-1109. DOI: 10.3724/SP.J.1089.2019.17388

单幅室外图像的高阶能量方程阴影检测算法

Detecting Shadows from a Single Outdoor Image Based on High Order Energy Function

  • 摘要: 单幅室外图像的阴影检测是数字图像处理领域的研究热点之一.针对单幅室外图像的阴影检测算法往往只关注阴影的边缘或区域信息,忽视了两者之间的依存关系,即“阴影区域-阴影边缘-非阴影区域”的阴影布局信息的问题,提出阴影检测算法.首先将图像分割成独立区域;然后利用支持向量机构建高阶能量方程,对阴影布局信息进行建模;最后通过最小化方程来判断区域是否为阴影.文中不但严谨证明了高阶能量方程在局部最优点下的性质,而且指出了其最优的降阶方法.在公开图像数据库上的实验表明,该算法能够有效地检测单幅室外图像中的阴影区域.

     

    Abstract: Detecting shadows from a single outdoor image has been one of research hotspots in image processing. To solve the problem that existing algorithms often focused on either shadow edges or shadow areas and ignored their dependence, specifically a layout information of “shadow area - shadow edge - non-shadow area”, this paper proposed a shadow detection algorithm: firstly, an outdoor image was segmented into regions;secondly, the layout information was modeled by constructing a high order energy function with the help of support vector machine;finally, shadows were determined by minimizing this function. This paper also rigorously proved the property of the proposed high order energy function under the local optimum, and introduced an optimal method of reducing itself into a second order energy function. Experiments on public image databases show that the proposed algorithm can effectively detect shadows from a single outdoor image.

     

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