Rodrigo de Bem
Rodrigo de Bem
Department of Engineering Science, University of Oxford
Email confirmado em robots.ox.ac.uk - Página inicial
TítuloCitado porAno
C-NLPCA: Extracting Non-Linear Principal Components of Image Datasets
SSC Botelho, R de Bem, ÍL Almeida, MM Mata
XII Simpósio Brasileiro de Sensoriamento Remoto (SBSR), 3495-3502, 2005
172005
Analyzing and exploring feature detectors in images
P Drews, R de Bem, A de Melo
9th IEEE International Conference on Industrial Informatics (INDIN), 305-310, 2011
122011
3d hand shape and pose from images in the wild
A Boukhayma, R de Bem, PHS Torr
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
102019
A model-free approach for object tracking in sequences of images
R de Bem
University of São Paulo, 2007
5*2007
Applying neural networks to study the mesoscale variability of oceanic boundary currents
SSC Botelho, MM Mata, R de Bem, I Almeida
14th Intl Symposium on Methodologies for Intelligent Systems (ISMIS), 684-688, 2003
52003
Hardware and Software Infrastructure to Image Acquisition using Multiple Cameras
D Thiel, M Goulart, S Botelho, R de Bem
XXVI Conference on Graphics, Patterns, and Images (SIBGRAPI) - Workshop of …, 2013
4*2013
A conditional deep generative model of people in natural images
R de Bem, A Ghosh, A Boukhayma, T Ajanthan, N Siddharth, P Torr
IEEE Winter Conference on Applications of Computer Vision (WACV), 1449-1458, 2019
32019
Deep fully-connected part-based models for human pose estimation
R de Bem, A Arnab, S Golodetz, M Sapienza, P Torr
Asian Conference on Machine Learning (ACML), 327-342, 2018
32018
DGPose: Disentangled Semi-supervised Deep Generative Models for Human Body Analysis
R de Bem, A Ghosh, T Ajanthan, O Miksik, N Siddharth, PHS Torr
arXiv preprint arXiv:1804.06364, 2018
32018
A semi-supervised deep generative model for human body analysis
R de Bem, A Ghosh, T Ajanthan, O Miksik, N Siddharth, P Torr
European Conference on Computer Vision (ECCV) Workshops, 2018
32018
3D motion tracking based on probabilistic volumetric reconstruction and optical flow
GM Simas, GP Fickel, L Novelo, R de Bem, SSC Botelho
23rd Conference on Graphics, Patterns and Images (SIBGRAPI), 279-286, 2010
32010
Utilizando visão computacional para reconstrução probabilística 3D e rastreamento de movimento
GM Simas, GP Fickel, L Novelo, R de Bem, SSC Botelho
VETOR-Revista de Ciências Exatas e Engenharias 17 (2), 59-77, 2007
32007
Rastreamento visual de múltiplos objetos utilizando uma abordagem livre de modelo
R de Bem, AHR Costa
XVI Congresso Brasileiro de Automática (CBA), 2760-2765, 2006
32006
A 3d motion tracking method based on nonparametric belief propagation
G Simas, R de Bem, S Botelho
IEEE International Conference on Robotics and Automation (ICRA), 1616-1622, 2013
12013
3D Representation Models Construction through a Volume Geometric Decomposition Method
G Simas, R de Bem, S Botelho
8th Intl Conference on Computer Vision Theory and Applications (VISAPP), 274-279, 2013
12013
Using computer vision for 3d probabilistic reconstruction and motion tracking
GM Simas, GP Fickel, L Novelo, SSC Botelho, R de Bem
3rd Southern Conference on Computational Modeling (MCSUL), 119-124, 2009
12009
Rastreamento Visual de Multiplos Objetos Baseado em Contornos Aplicado ao Futebol de Robôs
R de Bem, AHR Costa
III Encontro de Robótica Inteligente (EnRI) / XXVI Congresso da SBC, 163-172, 2006
1*2006
Cascaded Nonlinear Principal Components Analysis: an application in extraction of human movements from video sequences
M Figueiredo, S Botelho, R de Bem, TM Centeno, W Lautenschlager
VII Simpósio Brasileiro de Automação Inteligente (SBAI) / II IEEE Latin …, 2005
12005
Critical Percolation as a Framework to Analyze the Training of Deep Networks
Z Ringel, R de Bem
6th International Conference on Learning Representations (ICLR), 2018
2018
Looking deep at people: towards understanding and generating humans in images with deep learning
RA de Bem
University of Oxford, 2018
2018
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Artigos 1–20