首页> 外文期刊>Visualization and Computer Graphics, IEEE Transactions on >Detection and Reconstruction of an Implicit Boundary Surface by Adaptively Expanding A Small Surface Patch in a 3D Image
【24h】

Detection and Reconstruction of an Implicit Boundary Surface by Adaptively Expanding A Small Surface Patch in a 3D Image

机译:通过自适应扩展3D图像中的小曲面补丁来检测和重建隐式边界曲面

获取原文
获取原文并翻译 | 示例

摘要

In this paper we propose a novel and easy to use 3D reconstruction method. With the method, users only need to specify a small boundary surface patch in a 2D section image, and then an entire continuous implicit boundary surface (CIBS) can be automatically reconstructed from a 3D image. In the method, a hierarchical tracing strategy is used to grow the known boundary surface patch gradually in the 3D image. An adaptive detection technique is applied to detect boundary surface patches from different local regions. The technique is based on both context dependence and adaptive contrast detection as in the human vision system. A recognition technique is used to distinguish true boundary surface patches from the false ones in different cubes. By integrating these different approaches, a high-resolution CIBS model can be automatically reconstructed by adaptively expanding the small boundary surface patch in the 3D image. The effectiveness of our method is demonstrated by its applications to a variety of real 3D images, where the CIBS with complex shapes/branches and with varying gray values/gradient magnitudes can be well reconstructed. Our method is easy to use, which provides a valuable tool for 3D image visualization and analysis as needed in many applications.
机译:在本文中,我们提出了一种新颖且易于使用的3D重建方法。使用该方法,用户只需要在2D截面图像中指定较小的边界面补丁,然后可以从3D图像自动重建整个连续的隐式边界面(CIBS)。在该方法中,使用分层跟踪策略来逐渐在3D图像中增长已知的边界面斑。应用自适应检测技术来检测来自不同局部区域的边界表面斑块。与人类视觉系统一样,该技术基于上下文依赖和自适应对比度检测。识别技术用于区分不同立方体中的真实边界面块与假边界面块。通过集成这些不同的方法,可以通过自适应地扩展3D图像中的小边界面补丁来自动重建高分辨率CIBS模型。我们的方法在各种真实3D图像中的应用证明了我们方法的有效性,其中可以很好地重建具有复杂形状/分支和不同灰度值/渐变幅度的CIBS。我们的方法易于使用,可为许多应用程序中所需的3D图像可视化和分析提供有价值的工具。

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