This article introduces a novel approach to generate visually promising skeletons automatically without any manual tuning. In practice, it is challenging to extract promising skeletons directly using existing approaches. This is because they either cannot fully preserve shape features, or require manual intervention, such as boundary smoothing and skeleton pruning, to justify the eye-level view assumption. We propose an approach here that generates backbone and dense skeletons by shape input, and then extends the backbone branches via skeleton grafting from the dense skeleton to ensure a well-integrated output. Based on our evaluation, the generated skeletons best depict the shapes at levels that are similar to human perception. To evaluate and fully express the properties of the extracted skeletons, we introduce two potential functions within the high-order matching protocol to improve the accuracy of skeleton-based matching. These two functions fuse the similarities between skeleton graphs and geometrical relations characterized by multiple skeleton endpoints. Experiments on three high-order matching protocols show that the proposed potential functions can effectively reduce the number of incorrect matches.

中文翻译：

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通过骨骼移植实现自动骨骼提取。

本文介绍了一种无需任何手动调整即可自动生成具有视觉效果的骨架的新方法。在实践中，直接使用现有方法提取有前途的骨架具有挑战性。这是因为它们要么不能完全保留形状特征，要么需要手动干预，例如边界平滑和骨架修剪，以证明眼睛水平视图假设的合理性。我们在这里提出了一种方法，通过形状输入生成主干和密集骨架，然后通过从密集骨架嫁接来扩展主干分支，以确保良好集成的输出。根据我们的评估，生成的骨架最好地描绘了与人类感知水平相似的形状。为了评估和充分表达提取的骨骼的属性，我们在高阶匹配协议中引入了两个潜在功能，以提高基于骨架的匹配的准确性。这两个函数融合了骨架图和以多个骨架端点为特征的几何关系之间的相似性。在三种高阶匹配协议上的实验表明，所提出的势函数可以有效地减少不正确匹配的数量。