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Low-Rank Representation (LRR) is an effective self-expressiveness method, which uses the observed data itself as the dictionary to reconstruct the original data. LRR focuses on representing the global low-dimensional information, but ignores the real fact that data often resides on low-dimensional manifolds embedded in a high-dimensional data. Therefore, LRR can not capture the non-linear geometric...
This paper presents a multilevel analysis of 2D shapes and uses it to find similarities between the different parts of a shape. Such an analysis is important for many applications such as shape comparison, editing, and compression. Our robust and stable method decomposes a shape into parts, determines a parts hierarchy, and measures similarity between parts based on a salience measure on the medial...
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