Yang Cong, Indurkhya Bipin, See John, Grzegorzek Marcin
IEEE Trans Vis Comput Graph. 2021 Dec;27(12):4520-4532. doi: 10.1109/TVCG.2020.3003994. Epub 2021 Oct 26.
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.
本文介绍了一种无需任何手动调整即可自动生成视觉上有吸引力的骨架的新方法。在实践中，直接使用现有方法提取有吸引力的骨架具有挑战性。这是因为它们要么不能完全保留形状特征，要么需要人工干预，如边界平滑和骨架修剪，以证明平视视图假设的合理性。我们在此提出一种方法，通过形状输入生成主干骨架和密集骨架，然后通过从密集骨架进行骨架嫁接来扩展主干分支，以确保输出的良好整合。基于我们的评估，生成的骨架在与人类感知相似的水平上最能描绘形状。为了评估和充分表达提取骨架的属性，我们在高阶匹配协议中引入了两个势函数，以提高基于骨架匹配的准确性。这两个函数融合了骨架图之间的相似性以及由多个骨架端点表征的几何关系。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减少错误匹配的数量。
