1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchAntonio Criminisihttps://www.microsoft.com/en-us/research/people/antcrim/Sparse Bayesian Registration - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="OXt9Toib8M"><a href="https://www.microsoft.com/en-us/research/publication/sparse-bayesian-registration/">Sparse Bayesian Registration</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/sparse-bayesian-registration/embed/#?secret=OXt9Toib8M" width="600" height="338" title="“Sparse Bayesian Registration” — Microsoft Research" data-secret="OXt9Toib8M" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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We propose a Sparse Bayesian framework for non-rigid registration. Our principled approach is flexible, in that it efficiently finds an optimal, sparse model to represent deformations among any preset, widely overcomplete range of basis functions. It addresses open challenges in state-of-the-art registration, such as the automatic joint estimate of model parameters (e.g. noise and regularization […]