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of 209
pro vyhledávání: '"Sahin, Yusuf"'
Point clouds are extensively employed in a variety of real-world applications such as robotics, autonomous driving and augmented reality. Despite the recent success of point cloud neural networks, especially for safety-critical tasks, it is essential
Externí odkaz:
http://arxiv.org/abs/2403.06698
Point clouds and meshes are widely used 3D data structures for many computer vision applications. While the meshes represent the surfaces of an object, point cloud represents sampled points from the surface which is also the output of modern sensors
Externí odkaz:
http://arxiv.org/abs/2403.06661
Publikováno v:
Porto, Portugal: SIS-Symmetry (2022) pp. 96-104
This study integrates artificial intelligence and computational design tools to extract information from architectural heritage. Photogrammetry-based point cloud models of brick walls from the Anatolian Seljuk period are analysed in terms of the inte
Externí odkaz:
http://arxiv.org/abs/2210.12856
In recent years, deep learning based methods have shown success in essential medical image analysis tasks such as segmentation. Post-processing and refining the results of segmentation is a common practice to decrease the misclassifications originati
Externí odkaz:
http://arxiv.org/abs/2108.03117
Autor:
Kivrak, Ahmet1 a.kivrak89@gmail.com, Sahin, Yusuf Bozkurt1, Akdogan, Murat1, Akgun, Onur1, Tunca, Cagatay1, Guneş, Ozan1, Taskin, Yusuf1, Tanik, Veysel Ozan1, Ates, Ahmet Hakan2
Publikováno v:
Journal of Clinical Practice & Research. Mar2024, Vol. 46 Issue 2, p191-201. 11p.
Learning new representations of 3D point clouds is an active research area in 3D vision, as the order-invariant point cloud structure still presents challenges to the design of neural network architectures. Recent works explored learning either globa
Externí odkaz:
http://arxiv.org/abs/2012.04708
We propose a new approach for the problem of relative depth estimation from a single image. Instead of directly regressing over depth scores, we formulate the problem as estimation of a probability distribution over depth and aim to learn the paramet
Externí odkaz:
http://arxiv.org/abs/2010.07091
Deep neural network training without pre-trained weights and few data is shown to need more training iterations. It is also known that, deeper models are more successful than their shallow counterparts for semantic segmentation task. Thus, we introdu
Externí odkaz:
http://arxiv.org/abs/2009.06469
Akademický článek
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Publikováno v:
Scripta Medica, Vol 54, Iss 3, Pp 267-272 (2023)
Background/Aim: Surgeon experience, which is an important factor in reducing surgical complications, has been underestimated when analysing percutaneous nephrolithotomy (PNL) outcomes. Aim of this study was to investigate the impact of annual case vo
Externí odkaz:
https://doaj.org/article/1059cbdd92ad4490a61a608c14515371