Interactive grain image segmentation using graph cut algorithms
Interactive grain image segmentation using graph cut algorithms
复制标题
使用图割算法进行交互式颗粒图像分割
DOI：
10.1117/12.2014161
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Song Wang
中科院分区：
文献类型：
--
作者：
Jarrell W. Waggoner;Youjie Zhou;J. Simmons;Ayman Salem;M. Graef;Song Wang
材料图像的分割是一个费时费力的过程，自动图像分割算法通常存在缺陷和错误。交互式分割是图像处理和计算机视觉领域的一个日益增长的主题，它寻求全自动方法和全手动分割过程之间的平衡。通过在其他自动算法中允许来自用户的最小化和简单化的交互，交互式分割能够同时减少分割图像所花费的时间，同时实现更好的分割结果。鉴于材料图像的特殊结构和所需的分割质量水平，我们展示了一个用于材料图像的交互式分割框架，该框架具有两个关键贡献：1）多标记框架，其可以处理大量结构，同时仍然快速且方便地允许实时手动交互，以及2）参数估计方法，其防止用户必须手动指定参数，增加了交互的简单性。我们展示了一个完整的配方，这些贡献和例子的结果，从他们的应用。
Segmenting materials images is a laborious and time-consuming process and automatic image segmentation algorithms usually contain imperfections and errors. Interactive segmentation is a growing topic in the areas of image processing and computer vision, which seeks to and a balance between fully automatic methods and fully manual segmentation processes. By allowing minimal and simplistic interaction from the user in an otherwise automatic algorithm, interactive segmentation is able to simultaneously reduce the time taken to segment an image while achieving better segmentation results. Given the specialized structure of materials images and level of segmentation quality required, we show an interactive segmentation framework for materials images that has two key contributions: 1) a multi-labeling framework that can handle a large number of structures while still quickly and conveniently allowing manual interaction in real-time, and 2) a parameter estimation approach that prevents the user from having to manually specify parameters, increasing the simplicity of the interaction. We show a full formulation of each of these contributions and example results from their application.