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首页> 外文会议>SIBGRAPI Conference on Graphics, Patterns and Images >Oriented Image Foresting Transform Segmentation: Connectivity Constraints with Adjustable Width
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Oriented Image Foresting Transform Segmentation: Connectivity Constraints with Adjustable Width

机译：定向图像森林变换分割：宽度可调的连接约束

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摘要

In this work, we extend a novel seed-based segmentation algorithm, which provides global optimum solutions according to a graph-cut measure, subject to high-level boundary constraints: The simultaneously handling of boundary polarity and connectivity constraints. The proposed method incorporates the connectivity constraint in the Oriented Image Foresting Transform (OIFT), ensuring the generation of connected objects, but such that the connection between its internal seeds is guaranteed to have a user-controllable minimum width. In other frameworks, such as the min-cut/max-flow algorithm, the connectivity constraint is known to lead to NP-hard problems. In contrast, our method conserves the low complexity of the OIFT algorithm. In the experiments, we show improved results for the segmentation of thin and elongated objects, for the same amount of user interaction. Our dataset of natural images with true segmentation is publicly available to the community.
机译：在这项工作中，我们扩展了一种新颖的基于种子的分割算法，该算法根据图割措施提供全局最优解，并且要服从高级边界约束：边界极性和连接性约束的同时处理。所提出的方法在定向图像森林变换（OIFT）中合并了连接性约束，从而确保了生成连接对象，但是可以确保其内部种子之间的连接具有用户可控制的最小宽度。在其他框架中，例如最小切割/最大流量算法，已知连接限制会导致NP难题。相反，我们的方法保留了OIFT算法的低复杂度。在实验中，对于相同数量的用户交互，我们显示了细分对象和细长对象的改进结果。我们的具有真实细分的自然图像数据集可向社区公开提供。

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