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A perceptual quality metric for 3D triangle meshes based on spatial pooling

Xiang Feng, Wanggen Wan, Richard Yi Da Xu, Haoyu Chen, Pengfei Li, J. Alfredo Sánchez
2018 Frontiers of Computer Science  
With the feature vector as input, we adopt Support Vector Regression model to predict the mesh quality score.  ...  In this paper, we propose a new objective quality metric for assessing the visual difference between a reference mesh and a corresponding distorted mesh.  ...  Specifically, we adopt Support Vector Regression model as the learning model, which will be introduced in detail in Section 5.  ... 
doi:10.1007/s11704-017-6328-x fatcat:hmd2cds4abedfawagv6x25jvma

CPC‐GSCT: Visual quality assessment for coloured point cloud based on geometric segmentation and colour transformation

Lei Hua, Mei Yu, Zhouyan He, Renwei Tu, Gangyi Jiang
2021 IET Image Processing  
Aiming at this problem, this paper proposes a new full-reference visual quality assessment metric for CPC based on geometric segmentation and colour transformation (CPC-GSCT), which analyzes geometric  ...  Coloured point cloud (CPC) is one of the important representations of three-dimensional objects, which has been used in many fields.  ...  For analysis of quality regression, RF is compared with two representative regression algorithms used in predicting quality, that is, General Regression Neural network (GRN) [36] and Support Vector Regression  ... 
doi:10.1049/ipr2.12211 fatcat:ok2qhomdwzhgxg3bsbjyaavhvy

Point Cloud Quality Assessment: Dataset Construction and Learning-based No-Reference Approach [article]

Yipeng Liu, Qi Yang, Yiling Xu, Le Yang
2021 arXiv   pre-print
Few researches about NR objective quality metrics are conducted due to the lack of a large-scale subjective point cloud dataset.  ...  However, in many cases, obtaining the reference point cloud is difficult, so the no-reference (NR) methods have become a research hotspot.  ...  [47] propose a quality assessment metric based on the color histogram. Alexiou et al.  ... 
arXiv:2012.11895v3 fatcat:q5ujea4ewbbr5k773hatjgbpw4

Feature Sensitive Three-Dimensional Point Cloud Simplification using Support Vector Regression

2019 Tehnički Vjesnik  
In this paper we propose a method for feature sensitive simplification of 3D point clouds that is based on ε insensitive support vector regression (ε-SVR).  ...  Contemporary three-dimensional (3D) scanning devices are characterized by high speed and resolution.  ...  Acknowledgements This research was partially supported by Serbian Ministry of Education, Science and Technological Development, under research grants TR35004 and TR35020.  ... 
doi:10.17559/tv-20180328175336 fatcat:2u56wj33zzfatjyli2an6dkesu

Impact of Aging on Three-Dimensional Facial Verification

Majid Zadeh Heravi, Farazdaghi, Fournier, Nait-ali
2019 Electronics  
A performance evaluation was completed based on three metrics: structural texture quality, mesh geometric distortion and morphometric landmark distances.  ...  For this purpose, we employed three-dimensional (3D) faces obtained from a 3D morphable face aging model (3D F-FAM).  ...  The aforementioned methods come with certain limitations since they are mostly based on two-dimensional representations [25] .  ... 
doi:10.3390/electronics8101170 fatcat:ojuorrwvejaffmzmufbmtrkxai

Models for predicting and explaining citation count of biomedical articles

Lawrence D Fu, Constantin Aliferis
2008 AMIA Annual Symposium Proceedings  
Our experiments show that it is indeed feasible to accurately predict future citation counts with a mixture of content-based and bibliometric features using machine learning methods.  ...  This metric however is unavailable until several years after publication time.  ...  It presents a regression model to predict citation counts in a time horizon of two years based on information available within three weeks of publication.  ... 
pmid:18999029 pmcid:PMC2656101 fatcat:d6ggiqbuefhahit5yiptlp2g6e

An SVM learning approach to robotic grasping

R. Pelossof, A. Miller, P. Allen, T. Jebara
2004 IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004  
Our unique appmach to the problem involves a combination of numerical methods to mover parts of the grasp quality surface with any robotic hand, and contemporaj machine learning methods to interpolate  ...  Absfmct-Finding appropriate stable grasps for a hand (either mhotic or human) on an arbitrary object has proved to .he a challenging and difficult problem.  ...  ACKNOWLEDGMENT This work was supported in part by an NSF-ITR award IIS-03-12693.  ... 
doi:10.1109/robot.2004.1308797 dblp:conf/icra/PelossofMAJ04 fatcat:aqihuarn5nchpidn5xkrz2alh4

Prediction of geometry deviations in additive manufactured parts: comparison of linear regression with machine learning algorithms

Ivanna Baturynska, Kristian Martinsen
2020 Journal of Intelligent Manufacturing  
In this work, machine learning techniques are applied for the same data, and results are compared with the previously reported linear models. The linear regression model is the best for width.  ...  Dimensional accuracy in additive manufacturing (AM) is still an issue compared with the tolerances for injection molding.  ...  Support vector regression Support vector regression (SVR) is a type of Support Vector Machine techniques that tackle the regression tasks.  ... 
doi:10.1007/s10845-020-01567-0 fatcat:oj3u4euprzbvlavik45ur5c7aq

3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric Fusion-Net of Demographic Properties [article]

Soha Sadat Mahdi
2020 arXiv   pre-print
Furthermore, the proposed neural-based pipeline outperforms a linear baseline, which consists of principal component analysis, followed by classification with linear support vector machines and a Naive  ...  The first step consists of a triplet loss-based metric learner that compresses facial shape into a lower dimensional embedding while preserving information about the property of interest.  ...  Metric learning refers to the task of learning a semantic representation of data, based on the similarity measures defined by optimal distance metrics [4] .  ... 
arXiv:2009.04746v2 fatcat:ldxv3zqkjjfafp2ko5sdurgugy

3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric Fusion-Net of Demographic Properties

Soha Sadat Mahdil, Nele Nauwelaers, Philip Joris, Giorgos Bouritsas, Shunwang Gong, Sergiy Bokhnyak, Susan Walsh, Mark D. Shriver, Michael Bronstein, Peter Claes
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
Furthermore, the proposed neural-based pipeline outperforms a linear baseline, which consists of principal component analysis, followed by classification with linear support vector machines and a Naïve  ...  The first step consists of a triplet loss-based metric learner that compresses facial shape into a lower dimensional embedding while preserving information about the property of interest.  ...  Metric learning refers to the task of learning a semantic representation of data, based on the similarity measures defined by optimal distance metrics [4] .  ... 
doi:10.1109/icpr48806.2021.9412166 fatcat:npcxtycb4fcv7lidjb3gfjaauu

Deep Learning for Automated Brain Tumor Segmentation in MRI Images [chapter]

Rupal R. Agravat, Mehul S. Raval
2018 Soft Computing Based Medical Image Analysis  
In this paper a simple strategy for the automatic segmentation of tissues in magnetic resonance images of multispectral classification based mainly on minimum Euclidean distance is presented From a set  ...  In the present work, a method based on multidimensional mathematical morphology is used to classify brain tissues for multimodality MRI comprising 4 modalities, allowing for tumor image segmentation and  ...  of the criterion based on intensities relative getting soft dimensional meshes with a precision to indicators texture, while use of the algorithm improves sub-voxel.  ... 
doi:10.1016/b978-0-12-813087-2.00010-5 fatcat:l4mlyo7635cqtpgrdhsor6z4ty

Predicting the Risk of Alcohol Use Disorder Using Machine Learning: A Systematic Literature Review

Ali Ebrahimi, Uffe Kock Wiil, Thomas Schmidt, Amin Naemi, Anette Sxgaard Nielsen, Ghulam Mujtaba Shaikh, Marjan Mansourvar
2021 IEEE Access  
Support vector machine was the most widely employed algorithm for predicting AUD; however, the lack of deep neural network techniques is evident in this field.  ...  Six bibliographic databases were searched, and the identified studies were rigorously reviewed based on the above five aspects.  ...  Anne Faber Hansen, a research librarian at the University of Southern Denmark, for helping in the preparation of search keywords and queries.  ... 
doi:10.1109/access.2021.3126777 fatcat:vsp2egxeqza4beu7gfbqo3hqly

Reflective random indexing for semi-automatic indexing of the biomedical literature

Vidya Vasuki, Trevor Cohen
2010 Journal of Biomedical Informatics  
Currently, the Medical Text Indexer (MTI) system provides assistance by recommending MeSH terms based on the title and abstract of an article using a combination of distributional and vocabulary-based  ...  In this paper, we evaluate a novel approach toward indexer assistance by using nearest neighbor classification in combination with Reflective Random Indexing (RRI), a scalable alternative to the established  ...  Acknowledgments We would like to thank Dominic Widdows for creating the Semantic Vectors Package and NLM for providing the MEDLINE database that facilitated our research.  ... 
doi:10.1016/j.jbi.2010.04.001 pmid:20382265 fatcat:hcikk4b325drbcfwc6vzpgtlx4

A mesh optimization method using machine learning technique and variational mesh adaptation

Tingfan WU, Xuejun LIU, Wei AN, Zenghui HUANG, Hongqiang LYU
2021 Chinese Journal of Aeronautics  
from the estimated flow field on the updated mesh via the regression model.  ...  A mesh optimization method, which embeds a machine learning regression model into the variational mesh adaptation, is proposed.  ...  There are many regression methods, such as linear regression, support vector machine regression, Gaussian process regression, neural network, etc.  ... 
doi:10.1016/j.cja.2021.05.018 fatcat:7ftpfzkudrdvflh3edpgsz2ceq

A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery

Paul G. M. Knoops, Athanasios Papaioannou, Alessandro Borghi, Richard W. F. Breakey, Alexander T. Wilson, Owase Jeelani, Stefanos Zafeiriou, Derek Steinbacher, Bonnie L. Padwa, David J. Dunaway, Silvia Schievano
2019 Scientific Reports  
outcomes with a mean accuracy of 1.1 ± 0.3 mm.  ...  Here, we present the first large-scale clinical 3D morphable model, a machine-learning-based framework involving supervised learning for diagnostics, risk stratification, and treatment simulation.  ...  This work was undertaken at GOSH/ICH, UCLH/ UCL who received a proportion of funding from the United Kingdom Department of Health's NIHR Biomedical Research Centre funding scheme.  ... 
doi:10.1038/s41598-019-49506-1 pmid:31537815 pmcid:PMC6753131 fatcat:u3ahyikw2rbazc2mk5wiwywyke
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