Results 51 to 60 of about 8,755 (208)

Some Properties of Gromov–Hausdorff Distances [PDF]

Discrete & Computational Geometry, 2012
Let \({\mathcal G}\) stand for the class of all compact metric spaces and let \(GH(.,.)\) be the Gromov-Hausdorff distance on it. In this paper, a modified Gromov-Hausdorff distance is introduced as \(\widehat{GH}(X,Y)= (1/2)\max\{\text{infdis}(X\to Y),\text{infdis}(Y\to X)\}\), \(X,Y\in {\mathcal G}\).
openaire   +3 more sources

Clinical Applications of Multimodal Artificial Intelligence in Otolaryngology: A State‐of‐the‐Art Review

Otolaryngology–Head and Neck Surgery, EarlyView.
Abstract Objective Artificial intelligence (AI) has advanced to simultaneously process visual, auditory, and textual inputs, providing users with “multimodal” AI. Given the clinical integration potential of these tools, otolaryngologists must stay informed. This study reviews current literature on applications of multimodal AI in otolaryngology.
Ying Jie Li, Flora Su, Norbert Banyi, Philip Edgcumbe, Andrew Thamboo, Ameen Amanian   +5 more
wiley   +1 more source

EXTENDED HAUSDORFF DISTANCE AND SIMILARITY MEASURES FOR NEUTROSOPHIC REFINED SETS AND THEIR APPLICATION IN MEDICAL DIAGNOSIS

Journal of New Theory, 2015
In this paper we present a new distance measure between neutrosophic refined sets on the basis of extended Hausdorff distance of neutrosophic set and we study some of their basic properties.
Said Broumi, Florentin Smarandache
doaj  

A (p,q)-Averaged Hausdorff Distance for Arbitrary Measurable Sets

Mathematical and Computational Applications, 2018
The Hausdorff distance is a widely used tool to measure the distance between different sets. For the approximation of certain objects via stochastic search algorithms this distance is, however, of limited use as it punishes single outliers.
Johan M. Bogoya, Andrés Vargas, Oliver Cuate, Oliver Schütze   +3 more
doaj   +1 more source

Renormalization techniques for inflation systems and some of their applications

Acta Crystallographica Section A, EarlyView.
In this work, renormalization methods for quantities related to the diffraction of inflation systems are surveyed.Exact renormalization techniques are important and powerful, particularly for inflation‐generated systems. We review recent results in this direction.
Michael Baake, Franz Gähler, Anna Klick, Neil Mañibo, Jan Mazáč   +4 more
wiley   +1 more source

Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy

Physics and Imaging in Radiation Oncology, 2020
Background and purpose: In radiotherapy, automatic organ-at-risk segmentation algorithms allow faster delineation times, but clinically relevant contour evaluation remains challenging.
Femke Vaassen, Colien Hazelaar, Ana Vaniqui, Mark Gooding, Brent van der Heyden, Richard Canters, Wouter van Elmpt   +6 more
doaj   +1 more source

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, Morgan Denyer, Rachel Denyer, Howell Edwards   +3 more
wiley   +1 more source

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, Florine Hartwig, Josua Sassen, Sergio Conti, Mirela Ben‐Chen, Martin Rumpf   +5 more
wiley   +1 more source

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
wiley   +1 more source

LeafFit: Plant Assets Creation from 3D Gaussian Splatting

Computer Graphics Forum, EarlyView.
Abstract We propose LeafFit, a pipeline that converts 3D Gaussian Splatting (3DGS) of individual plants into editable, instanced mesh assets. While 3DGS faithfully captures complex foliage, its high memory footprint and lack of mesh topology make it incompatible with traditional game production workflows. We address this by leveraging the repetition of
Chang Luo, Nobuyuki Umetani
wiley   +1 more source