Results 61 to 70 of about 3,112 (208)
Computational aspects of the Hausdorff distance in unbounded dimension
Journal of Computational Geometry, 2014 We study the computational complexity of determining the Hausdorff distance oftwo polytopes given in halfspace- or vertex-presentation in arbitrary dimension.
Stefan König
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One‐Class Autoencoders for Porcelain Art Attribution: The Case of William Billingsley
Archaeometry, EarlyView.ABSTRACT This comprehensive study explores the application of advanced machine learning techniques, specifically one‐class autoencoders, for the authentication and attribution of English porcelain artworks. Focusing primarily on the works of William Billingsley (1758–1828), one of England's most celebrated porcelain decorators, we demonstrate how ...
Hassan Ugail +3 more
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Cantor spectrum for multidimensional quasi-periodic Schrödinger operators
Forum of Mathematics, SigmaIn this paper, we prove that for a dense set of irrational frequencies with positive Hausdorff dimension, the Hausdorff (and upper box) dimension of the spectrum of the critical almost Mathieu operator is positive, yet can be made arbitrarily small. As a
Bernard Helffer +3 more
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On the Billingsley dimension of Birkhoff average in the countable symbolic space
Comptes Rendus. Mathématique, 2020 We compute a lower bound of Billingsley–Hausdorff dimension, defined by Gibbs measure, of the level set related to Birkhoff average in the countable symbolic space $\mathbb{N}^{\mathbb{N}}$.
Attia, Najmeddine, Selmi, Bilel
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SDFs from Unoriented Point Clouds using Neural Variational Heat Distances
Computer Graphics Forum, EarlyView.We propose a novel variational approach for computing neural Signed Distance Fields (SDF) from unoriented point clouds. We first compute a small time step of heat flow (middle) and then use its gradient directions to solve for a neural SDF (right). Abstract We propose a novel variational approach for computing neural Signed Distance Fields (SDF) from ...
Samuel Weidemaier +5 more
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Basis Networks: Learning basis functions for free‐form triangulations
Computer Graphics Forum, EarlyView.Abstract We present a framework for learning compactly supported basis functions that define tangent continuous surfaces based on coarse irregular triangle meshes. The basis functions are represented as MLPs. Smoothness of the basis functions is achieved by using the values of Loop basis functions as the parameterization of the surface.
T. Djuren, M. Alexa
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Hausdorff Dimension of Centered Sets
Real Analysis Exchange, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Numerical estimates of Hausdorff dimension [PDF]
Journal of Computational Physics, 1982 Numerical methods for estimating Hausdorff dimension, useful in the analysis of turbulence, are explained and applied to a specific example. In particular, methods involving rescaling and approximation by Cantor sets are discussed.
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Survey on differential estimators for 3d point clouds
Computer Graphics Forum, EarlyView.Abstract Recent advancements in 3D scanning technologies, including LiDAR and photogrammetry, have enabled the precise digital replication of real‐world objects. These methods are widely used in fields such as GIS, robotics, and cultural heritage. However, the point clouds generated by such scans are often noisy and unstructured, posing challenges for ...
Léo Arnal–Anger +4 more
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Hausdorff Dimension and Gaussian Fields
The Annals of Probability, 1977 Let $X(t)$ be a Gaussian process taking values in $R^d$ and with its parameter in $R^N$. Then if $X_j$ has stationary increments and the function $\sigma^2(t) = E\{|X_j(s + t) - X_j(s)|^2\}$ behaves like $|t|^{2\alpha}$ as $|t| \downarrow 0, 0 < \alpha < 1$, the graph of $X$ has Hausdorff dimension $\min \{N/\alpha, N + d(1 - \alpha)\}$ with ...
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