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In terms of computational performance, our algorithm runs significantly faster than the most similar method to ours [ 17], which uses machine learning for classification and regularization on only one layer at a time.
We study the effect of this regularization on the order of accuracy for a one-dimensional time-dependent problem.
The Mumford-Shah model [2] is a classical variational segmentation method, which contains a data-fidelity term, regularization on the model, and regularization on the partitioning.
Thus, we obtain additional stronger regularization on the uniform and weaker regularization on the oscillatory patches, which significantly improves resulting image quality.
The influence of viscous regularization on symmetry modeling is also discussed.
This highlights the importance of regularization on the performance of DNN.
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They extract position invariant features using a sparse codebook on aligned images, and apply a local regularization framework on these features for automatic image annotation.
Registration is performed by optimizing a large scale numerical problem as given by a global objective function using one dense displacement field for each input volume and special regularization based on the time structure of the acquisition process.
Fig. 7 Regularization influence on the algorithms' performance.
where (||cdot ||_{F}^{2}) is the Frobenius norm, and Ω is the regularization constraint on S (t).
Therefore, methods, such as TV minimization, additionally apply regularization conditions on the reconstruction.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com