Hierarchical Framework for Shape Correspondence

  title={Hierarchical Framework for Shape Correspondence},
  author={Dan Raviv and Anastasia Dubrovina and Ron Kimmel},
Detecting similarity between non-rigid shapes is one of the fundamental problems in computer vision. In order to measure the similarity the shapes must first be aligned. As opposite to rigid alignment that can be parameterized using a small number of unknowns representing rotations, reflections and translations, non-rigid alignment is not easily parameterized. Majority of the methods addressing this problem boil down to a minimization of a certain distortion measure. The complexity of a… CONTINUE READING
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On Bending Invariant Signatures for Surfaces

IEEE Trans. Pattern Anal. Mach. Intell. • 2003
View 4 Excerpts
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Shape Recognition with Spectral Distances

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2011
View 1 Excerpt

Metric-induced optimal embedding for intrinsic 3D shape analysis

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition • 2010
View 1 Excerpt