From skeletons to bone graphs: Medial abstraction for object recognition
From skeletons to bone graphs: Medial abstraction for object recognition
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DOI：
10.1109/cvpr.2008.4587790
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发表时间：
2008-06
期刊：
影响因子：
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通讯作者：
Diego Macrini;Kaleem Siddiqi;Sven J. Dickinson
中科院分区：
文献类型：
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作者：
Diego Macrini;Kaleem Siddiqi;Sven J. Dickinson
中间描述（例如冲击图）由于其对平移、旋转、缩放和清晰度的不变性以及科普适度类内变形的能力，在基于形状的对象识别领域获得了显着的发展势头。当他们试图将一个形状分解成一组部分时，这种分解可能会受到结扎引起的不稳定性的影响。特别是，即使是一个很小的部分的添加可以在其附件附近的代表性产生巨大的影响。我们提出了一个算法，用于识别和表示的连字结构，并恢复非连字结构，仍然存在。这导致了一个骨图，一个新的内侧形状抽象，捕捉一个比骨架或冲击图更直观的概念的objectpsilas部分，并提供了改进的稳定性和类内变形不变性。我们证明了这些优势，通过比较使用骨图冲击图在一组基于视图的对象识别和姿态估计试验。
Medial descriptions, such as shock graphs, have gained significant momentum in the shape-based object recognition community due to their invariance to translation, rotation, scale and articulation and their ability to cope with moderate amounts of within-class deformation. While they attempt to decompose a shape into a set of parts, this decomposition can suffer from ligature-induced instability. In particular, the addition of even a small part can have a dramatic impact on the representation in the vicinity of its attachment. We present an algorithm for identifying and representing the ligature structure, and restoring the non-ligature structures that remain. This leads to a bone graph, a new medial shape abstraction that captures a more intuitive notion of an objectpsilas parts than a skeleton or a shock graph, and offers improved stability and within-class deformation invariance. We demonstrate these advantages by comparing the use of bone graphs to shock graphs in a set of view-based object recognition and pose estimation trials.