Skeleton pruning as trade-off between skeleton simplicity and reconstruction error
Skeleton pruning as trade-off between skeleton simplicity and reconstruction error
复制标题
骨架剪枝是骨架简单性和重建误差之间的权衡
DOI：
10.1007/s11432-012-4715-3
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发表时间：
2013-01
期刊：
影响因子：
--
通讯作者：
Longin Latecki
中科院分区：
文献类型：
--
作者：
Wei Shen;Xiang Bai;Xingwei Yang;Longin Latecki
骨架可以被视为紧凑的形状表示，因为每个形状都可以从其骨架完全重建。然而，骨架表示的实用性受到其不稳定性的严重限制。骨骼受到轮廓噪声的影响，因为小的轮廓变形可能会导致骨骼发生大的结构变化。为了解决这个问题，人们提出了大量的骨架计算和骨架剪枝方法。我们的方法有根本的不同，因为我们将骨架修剪作为骨架简单性和形状重建误差之间的权衡。给定形状的理想骨架应该是具有尽可能简单的结构的骨架，该骨架提供给定形状的最佳可能重建。为了量化这种权衡，我们提出骨架简单性对应于贝叶斯框架中的模型简单性，形状重建精度表示为数据的拟合优度。我们还提供了一种简单的算法来近似贝叶斯后验概率的最大值，该算法定义了迭代删除末端分支以获得修剪骨架的顺序。在没有任何参数调整的情况下获得的实验结果清楚地表明，所得骨架对于边界变形和类内形状变异是稳定的。
Skeletons can be viewed as a compact shape representation in that each shape can be completely reconstructed from its skeleton. However, the usefulness of a skeletal representation is strongly limited by its instability. Skeletons suffer from contour noise in that small contour deformation may lead to large structural changes in the skeleton. A large number of skeleton computation and skeleton pruning approaches has been proposed to address this issue. Our approach differs fundamentally in the fact that we cast skeleton pruning as a trade-off between skeleton simplicity and shape reconstruction error. An ideal skeleton of a given shape should be the skeleton with a simplest possible structure that provides a best possible reconstruction of a given shape. To quantify this trade-off, we propose that the skeleton simplicity corresponds to model simplicity in the Bayesian framework, and the shape reconstruction accuracy is expressed as goodness of fit to the data. We also provide a simple algorithm to approximate the maximum of the Bayesian posterior probability which defines an order for iteratively removing the end branches to obtain the pruned skeleton. Presented experimental results obtained without any parameter tuning clearly demonstrate that the resulting skeletons are stable to boundary deformations and intra class shape variability.
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影响因子：
19.5
作者：
Alex Levinshtein;C. Sminchisescu;Sven J. Dickinson
通讯作者：
Alex Levinshtein;C. Sminchisescu;Sven J. Dickinson
DOI：
10.1016/j.patcog.2010.08.021
发表时间：
2011-02
期刊：
Pattern Recognit.
影响因子：
--
作者：
W. Shen;X. Bai;Rong Hu;Hongyuan Wang;Longin Jan Latecki
通讯作者：
W. Shen;X. Bai;Rong Hu;Hongyuan Wang;Longin Jan Latecki
影响因子：
19.5
作者：
Song-Chun Zhu;A. Yuille
通讯作者：
Song-Chun Zhu;A. Yuille
DOI：
10.1109/cvpr.2008.4587790
发表时间：
2008-06
期刊：
2008 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
--
作者：
Diego Macrini;Kaleem Siddiqi;Sven J. Dickinson
通讯作者：
Diego Macrini;Kaleem Siddiqi;Sven J. Dickinson
DOI：
10.1109/cvpr.2000.855849
发表时间：
2000-06
期刊：
Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662)
影响因子：
--
作者：
P. Dimitrov;Carlos Phillips;Kaleem Siddiqi
通讯作者：
P. Dimitrov;Carlos Phillips;Kaleem Siddiqi