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Fitting Skeletal Object Models Using Spherical Harmonics Based Template Warping.

Liyun TuDan YangJared VicoryXiaohong ZhangStephen M PizerMartin Styner
Published in: IEEE signal processing letters (2015)
We present a scheme that propagates a reference skeletal model (s-rep) into a particular case of an object, thereby propagating the initial shape-related layout of the skeleton-to-boundary vectors, called spokes. The scheme represents the surfaces of the template as well as the target objects by spherical harmonics and computes a warp between these via a thin plate spline. To form the propagated s-rep, it applies the warp to the spokes of the template s-rep and then statistically refines. This automatic approach promises to make s-rep fitting robust for complicated objects, which allows s-rep based statistics to be available to all. The improvement in fitting and statistics is significant compared with the previous methods and in statistics compared with a state-of-the-art boundary based method.
Keyphrases
  • molecularly imprinted
  • working memory
  • deep learning
  • mass spectrometry
  • high resolution