Gaussian Process Regression with Fluid Hyperpriors

Girdziušas, Ramūnas; Laaksonen, Jorma

doi:10.1007/978-3-540-30499-9_87

Cited by 1 publication

(2 citation statements)

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“…We also discussed challenges associated with the invertibility of the landmark based transformation. An additional prior on this transformation, which ensures invertibility, similar to [2,12], would further improve the registration. This will be addressed in future work.…”

Section: Resultsmentioning

confidence: 99%

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Variational Image Registration Using Inhomogeneous Regularization

et al. 2014

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We present a generalization of the convolutionbased variational image registration approach, in which different regularizers can be implemented by conveniently exchanging the convolution kernel, even if it is nonseparable or nonstationary. Nonseparable kernels pose a challenge because they cannot be efficiently implemented by separate 1D convolutions. We propose to use a low-rank tensor decomposition to efficiently approximate nonseparable convolution. Nonstationary kernels pose an even greater challenge because the convolution kernel depends on, and needs to be evaluated for, every point in the image. We propose to pre-compute the local kernels and efficiently store them in memory using the Tucker tensor decomposition model. In our experiments we use the nonseparable exponential kernel and a nonstationary landmark kernel. The exponential kernel replicates desirable properties of elastic image registration, while the landmark kernel incorporates local prior knowledge about corresponding points in the images. We examine the trade-off between the computational resources needed and the approximation accuracy of the tensor decomposition methods. Furthermore, we obtain very smooth displacement fields even in the presence of large landmark displacements.

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Section: Resultsmentioning

confidence: 99%

“…An entirely different approach could be to perform diffeomorphic point matching [2,9,12,13] for obtaining a invertible mean displacement. This will be addressed in future work.…”

Section: Smooth Mean Displacementmentioning

confidence: 99%