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of 22 116
pro vyhledávání: '"shape representation"'
Geometric shape features have been widely used as strong predictors for image classification. Nevertheless, most existing classifiers such as deep neural networks (DNNs) directly leverage the statistical correlations between these shape features and
Externí odkaz:
http://arxiv.org/abs/2411.12201
Optimization-Driven Statistical Models of Anatomies using Radial Basis Function Shape Representation
Autor:
Xu, Hong, Elhabian, Shireen Y.
Publikováno v:
IEEE International Symposium on Biomedical Imaging (ISBI 2024)
Particle-based shape modeling (PSM) is a popular approach to automatically quantify shape variability in populations of anatomies. The PSM family of methods employs optimization to automatically populate a dense set of corresponding particles (as pse
Externí odkaz:
http://arxiv.org/abs/2411.15882
Autor:
Vutukur, Shishir Reddy, Ba, Mengkejiergeli, Busam, Benjamin, Kayser, Matthias, Singh, Gurprit
In this paper, we propose a novel encoder-decoder architecture, named SABER, to learn the 6D pose of the object in the embedding space by learning shape representation at a given pose. This model enables us to learn pose by performing shape represent
Externí odkaz:
http://arxiv.org/abs/2408.05867
Autor:
Ouasfi, Amine, Boukhayma, Adnane
Implicit Neural Representations have gained prominence as a powerful framework for capturing complex data modalities, encompassing a wide range from 3D shapes to images and audio. Within the realm of 3D shape representation, Neural Signed Distance Fu
Externí odkaz:
http://arxiv.org/abs/2408.15114
Autor:
Zhang, Congyi, Yang, Jinfan, Hedlin, Eric, Takikawa, Suzuran, Vining, Nicholas, Yi, Kwang Moo, Wang, Wenping, Sheffer, Alla
Compressed representations of 3D shapes that are compact, accurate, and can be processed efficiently directly in compressed form, are extremely useful for digital media applications. Recent approaches in this space focus on learned implicit or parame
Externí odkaz:
http://arxiv.org/abs/2409.06030
Autor:
Durve, Mihir, Tucny, Jean-Michel, Orsini, Sibilla, Tiribocchi, Adriano, Montessori, Andrea, Lauricella, Marco, Camposeo, Andrea, Pisignano, Dario, Succi, Sauro
We introduce a two-step, fully reversible process designed to project the outer shape of a generic droplet onto a lower-dimensional space. The initial step involves representing the droplet's shape as a Fourier series. Subsequently, the Fourier coeff
Externí odkaz:
http://arxiv.org/abs/2407.04863
Autor:
Keuth, Ron, Hansen, Lasse, Balks, Maren, Jäger, Ronja, Schröder, Anne-Nele, Tüshaus, Ludger, Heinrich, Mattias
Purpose: Semantic segmentation and landmark detection are fundamental tasks of medical image processing, facilitating further analysis of anatomical objects. Although deep learning-based pixel-wise classification has set a new-state-of-the-art for se
Externí odkaz:
http://arxiv.org/abs/2405.19746
In the field of computer vision, the numerical encoding of 3D surfaces is crucial. It is classical to represent surfaces with their Signed Distance Functions (SDFs) or Unsigned Distance Functions (UDFs). For tasks like representation learning, surfac
Externí odkaz:
http://arxiv.org/abs/2405.03381
Akademický článek
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Autor:
Durve, Mihir, Tucny, Jean-Michel, Bhamre, Deepesh, Tiribocchi, Adriano, Lauricella, Marco, Montessori, Andrea, Succi, Sauro
The shape of liquid droplets in air plays an important role in aerodynamic behavior and combustion dynamics of miniaturized propulsion systems such as microsatellites and small drones. Their precise manipulation can yield optimal efficiency in such s
Externí odkaz:
http://arxiv.org/abs/2403.15797