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樓主: osteomalacia
21#
發(fā)表于 2025-3-25 03:36:03 | 只看該作者
Exact Function Alignment Under Elastic Riemannian Metricstricted to their change points. In many cases, the computational cost for matching is reduced by orders of magnitude. We demonstrate the superiority of this method over the DPA using several simulated and real datasets.
22#
發(fā)表于 2025-3-25 10:45:05 | 只看該作者
23#
發(fā)表于 2025-3-25 15:00:38 | 只看該作者
Efficient Parallel Transport in the Group of Diffeomorphisms via Reduction to the Lie Algebrapressed as a linear ODE in the Lie algebra. Solving this ODE directly is numerically stable and significantly faster than other LDDMM parallel transport methods. Results on 2D synthetic data and 3D brain MRI demonstrate that our algorithm is fast and conserves the inner products of the transported tangent vectors.
24#
發(fā)表于 2025-3-25 18:40:03 | 只看該作者
https://doi.org/10.1007/978-1-4684-4778-1ies in the reconstruction or presenting alternative solutions. In this paper, we examine two different methods to sample vessel network graphs, a perturbation and a Gibbs sampler, and thereby estimate marginals. We quantitatively validate the accuracy of the approximated marginals using true marginals, computed by enumeration.
25#
發(fā)表于 2025-3-25 22:48:20 | 只看該作者
26#
發(fā)表于 2025-3-26 01:44:21 | 只看該作者
27#
發(fā)表于 2025-3-26 05:50:22 | 只看該作者
Bridge Simulation and Metric Estimation on?Landmark Manifoldsional PDE with no closed-form solution in the nonlinear case. We show how the density can be numerically approximated by Monte Carlo sampling of conditioned Brownian bridges, and we use this to estimate parameters of the LDDMM kernel and thus the metric structure by maximum likelihood.
28#
發(fā)表于 2025-3-26 10:25:33 | 只看該作者
29#
發(fā)表于 2025-3-26 15:01:34 | 只看該作者
30#
發(fā)表于 2025-3-26 19:17:13 | 只看該作者
Topology of Surface Displacement Shape Feature in Subcortical Structuresetween regions (e.g. subfields) of the structure and its change with disease remains unclear. In this paper, we present a first work to study the topology of the . shape feature via its persistence homology timeline features and model the polyadic interactions between the shape across the subfields
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