• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Information Technology Journal >2-D Cartoon Character Detection based on Scalable-Shape Context and Hough Voting
【24h】

2-D Cartoon Character Detection based on Scalable-Shape Context and Hough Voting

机译：基于可缩放形状上下文和霍夫投票的二维卡通人物检测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Cartoon pirate uploading is a very serious problem for the image and video-sharing website. In this study, we propose a new method to detect the characters in 2D-cartoon images, aiming at rejecting pirate uploading automatically. We extract the curve in the cartoon image as the main content and then design a local shape feature named Scalable-Shape Context (SSC) to present the local shape of cartoon. Firstly, we use the Harris-Laplace corner detector to find the key points at multi-scale in the cartoon image, most of which are localized at the junctions of curves. Secondly, the scale of each key point is used as a reference scale for Shape Context (SC) to describe the curvilinear structure around the key points. Then, the matching problem between the key points extracted from the input model and testing image is solved as an optimal assignment problem. Finally, a Hough-voting scheme is employed to find the location of the character in the testing image. The experimental results show that the proposed SSC-based detection method is effective in the detection of 2D-cartoon characters.
机译：对于图像和视频共享网站而言，卡通海盗上传是一个非常严重的问题。在这项研究中，我们提出了一种检测2D卡通图像中字符的新方法，旨在自动拒绝盗版者。我们提取卡通图像中的曲线作为主要内容，然后设计一个名为可缩放形状上下文（SSC）的局部形状特征，以呈现卡通的局部形状。首先，我们使用Harris-Laplace角检测器在卡通图像中找到多尺度的关键点，这些关键点大多位于曲线的交点处。其次，将每个关键点的比例用作形状上下文（SC）的参考比例，以描述关键点周围的曲线结构。然后，解决从输入模型提取的关键点与测试图像之间的匹配问题，作为最佳分配问题。最后，采用霍夫投票方案在测试图像中找到角色的位置。实验结果表明，所提出的基于SSC的检测方法对于二维卡通人物的检测是有效的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号