Comparing vector fields across surfaces: interest for characterizing the
orientations of cortical folds
- URL: http://arxiv.org/abs/2106.07470v1
- Date: Mon, 14 Jun 2021 14:56:44 GMT
- Title: Comparing vector fields across surfaces: interest for characterizing the
orientations of cortical folds
- Authors: Amine Bohi, Guillaume Auzias and Julien Lef\`evre
- Abstract summary: We propose a framework to transport the vector fields from the original surfaces onto a common domain.
The proposed framework enables the computation of statistics on vector fields.
It can be applied to different types of vector fields and surfaces, allowing for a large number of high potential applications in medical imaging.
- Score: 0.2578242050187029
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Vectors fields defined on surfaces constitute relevant and useful
representations but are rarely used. One reason might be that comparing vector
fields across two surfaces of the same genus is not trivial: it requires to
transport the vector fields from the original surfaces onto a common domain. In
this paper, we propose a framework to achieve this task by mapping the vector
fields onto a common space, using some notions of differential geometry. The
proposed framework enables the computation of statistics on vector fields. We
demonstrate its interest in practice with an application on real data with a
quantitative assessment of the reproducibility of curvature directions that
describe the complex geometry of cortical folding patterns. The proposed
framework is general and can be applied to different types of vector fields and
surfaces, allowing for a large number of high potential applications in medical
imaging.
Related papers
- An Intrinsic Vector Heat Network [64.55434397799728]
This paper introduces a novel neural network architecture for learning tangent vector fields embedded in 3D.
We introduce a trainable vector heat diffusion module to spatially propagate vector-valued feature data across the surface.
We also demonstrate the effectiveness of our method on the useful industrial application of quadrilateral mesh generation.
arXiv Detail & Related papers (2024-06-14T00:40:31Z) - On Wasserstein distances for affine transformations of random vectors [1.2836088204932843]
We give concrete lower bounds for rotated copies of random vectors in $mathbbR2$.
We derive upper bounds for compositions of affine maps which yield a fruitful variety of diffeomorphisms applied to an initial data measure.
We give a framework for mimicking handwritten digit or alphabet datasets that can be applied in a manifold learning framework.
arXiv Detail & Related papers (2023-10-05T23:30:41Z) - Neural Vector Fields: Implicit Representation by Explicit Learning [63.337294707047036]
We propose a novel 3D representation method, Neural Vector Fields (NVF)
It not only adopts the explicit learning process to manipulate meshes directly, but also the implicit representation of unsigned distance functions (UDFs)
Our method first predicts displacement queries towards the surface and models shapes as text reconstructions.
arXiv Detail & Related papers (2023-03-08T02:36:09Z) - Minimal Neural Atlas: Parameterizing Complex Surfaces with Minimal
Charts and Distortion [71.52576837870166]
We present Minimal Neural Atlas, a novel atlas-based explicit neural surface representation.
At its core is a fully learnable parametric domain, given by an implicit probabilistic occupancy field defined on an open square of the parametric space.
Our reconstructions are more accurate in terms of the overall geometry, due to the separation of concerns on topology and geometry.
arXiv Detail & Related papers (2022-07-29T16:55:06Z) - Learning Implicit Feature Alignment Function for Semantic Segmentation [51.36809814890326]
Implicit Feature Alignment function (IFA) is inspired by the rapidly expanding topic of implicit neural representations.
We show that IFA implicitly aligns the feature maps at different levels and is capable of producing segmentation maps in arbitrary resolutions.
Our method can be combined with improvement on various architectures, and it achieves state-of-the-art accuracy trade-off on common benchmarks.
arXiv Detail & Related papers (2022-06-17T09:40:14Z) - Neural Vector Fields for Implicit Surface Representation and Inference [73.25812045209001]
Implicit fields have recently shown increasing success in representing and learning 3D shapes accurately.
We develop a novel and yet a fundamental representation considering unit vectors in 3D space and call it Vector Field (VF)
We show the advantages of VF representation, in learning open, closed, or multi-layered as well as piecewise planar surfaces.
arXiv Detail & Related papers (2022-04-13T17:53:34Z) - Word2Box: Learning Word Representation Using Box Embeddings [28.080105878687185]
Learning vector representations for words is one of the most fundamental topics in NLP.
Our model, Word2Box, takes a region-based approach to the problem of word representation, representing words as $n$-dimensional rectangles.
We demonstrate improved performance on various word similarity tasks, particularly on less common words.
arXiv Detail & Related papers (2021-06-28T01:17:11Z) - A Differential Geometry Perspective on Orthogonal Recurrent Models [56.09491978954866]
We employ tools and insights from differential geometry to offer a novel perspective on orthogonal RNNs.
We show that orthogonal RNNs may be viewed as optimizing in the space of divergence-free vector fields.
Motivated by this observation, we study a new recurrent model, which spans the entire space of vector fields.
arXiv Detail & Related papers (2021-02-18T19:39:22Z) - Practical applications of metric space magnitude and weighting vectors [8.212024590297894]
The magnitude of a metric space is a real number that aims to quantify the effective number of distinct points in the space.
The contribution of each point to a metric space's global magnitude, which is encoded by the em weighting vector, captures much of the underlying geometry of the original metric space.
Surprisingly, when the metric space is Euclidean, the weighting vector also serves as an effective tool for boundary detection.
arXiv Detail & Related papers (2020-06-24T21:30:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.