• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IAENG transactions on engineering technologies >An Interactive Shadow Removing Tool: A Granular Computing Approach
【24h】

An Interactive Shadow Removing Tool: A Granular Computing Approach

机译：交互式阴影去除工具：粒度计算方法

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

This work proposes a tool to remove shadow from colour images with the help of user interaction. Shadow detection and removal is an interesting and a difficult image enhancement problem. In this work, a novel granule based approach for colour image enhancement is proposed. The proposed method constructs a shadow classifier using a Granular Reflex Fuzzy Min-Max Neural Network (GrRFMN). Classification and clustering techniques based on granular data are up-coming and finding importance in various fields including computer vision. GrRFMN capability to process granules of data is exploited here to tackle the problem of shadows. In this work, granule of data represents a group of pixels in the form of a hyperbox. During the training phase, GrRFMN learns shadow and non-shadow regions through an interaction with the user. A trained GrRFMN is then used to compute fuzzy memberships of image granules in the region of interest to shadow and non-shadow regions. A post processing of image based on the fuzzy memberships is then carried out to remove the shadow. As GrRFMN is trainable on-line in a single pass through data, the proposed method is fast enough to interact with the user.
机译：这项工作提出了一种在用户交互作用下从彩色图像中去除阴影的工具。阴影检测和去除是一个有趣且困难的图像增强问题。在这项工作中，提出了一种新颖的基于颗粒的彩色图像增强方法。所提出的方法使用粒度反射模糊最小-最大神经网络（GrRFMN）构造阴影分类器。基于粒状数据的分类和聚类技术正在兴起，并在包括计算机视觉在内的各个领域中找到了重要性。这里利用GrRFMN处理数据颗粒的能力来解决阴影问题。在这项工作中，数据粒度以超框的形式表示一组像素。在训练阶段，GrRFMN通过与用户的交互来学习阴影和非阴影区域。然后，将训练有素的GrRFMN用于计算感兴趣区域中阴影区域和非阴影区域的图像颗粒的模糊隶属度。然后基于模糊隶属度对图像进行后期处理以去除阴影。由于GrRFMN可在单次通过数据中进行在线训练，因此所提出的方法足够快，可以与用户进行交互。

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