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We present a novel method for shape analysis which represents shapes as probability density functions and then uses the intrinsic geometry of this space to match similar shapes. In our approach, shape densities are estimated by representing the square-root of the density in a wavelet basis. Under this model, each density (of a corresponding shape) is then mapped to a point on a unit hypersphere. For...
For advantages such as a richer representation power and inherent robustness to noise, probability density functions are becoming a staple for complex problems in shape analysis. We consider a principled and geometric approach to selecting the model order for a class of shape density models where the square-root of the distribution is expanded in an orthogonal series. The free parameters associated...
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