Jordao, Artur; Kloss, Ricardo; Yamada, Fernando; Schwartz, William Robson Pruning Deep Networks using Partial Least Squares Inproceedings British Machine Vision Conference (BMVC) Workshops, pp. 1-9, 2019. Links | BibTeX @inproceedings{Jordao:2019:BMVC,
title = {Pruning Deep Networks using Partial Least Squares},
author = {Artur Jordao and Ricardo Kloss and Fernando Yamada and William Robson Schwartz},
url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_BMVCW_Jordao.pdf},
year = {2019},
date = {2019-01-01},
booktitle = {British Machine Vision Conference (BMVC) Workshops},
pages = {1-9},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Kloss, Ricardo Barbosa; Jordao, Artur; Schwartz, William Robson Face Verification Strategies for Employing Deep Models Inproceedings 13th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 258-262, 2018. Links | BibTeX @inproceedings{Kloss:2018:FG,
title = {Face Verification Strategies for Employing Deep Models},
author = {Ricardo Barbosa Kloss and Artur Jordao and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Face-Verification-Strategies-for-Employing-Deep-Models.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {13th IEEE International Conference on Automatic Face & Gesture Recognition},
pages = {258-262},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Jordao, Artur; Junior, Antonio Carlos Nazare; Sena, Jessica; Schwartz, William Robson Human Activity Recognition based on Wearable Sensor Data: A Benchmark Journal Article arXiv, pp. 1-12, 2018. Links | BibTeX @article{Jordao:2018:arXiv,
title = {Human Activity Recognition based on Wearable Sensor Data: A Benchmark},
author = {Artur Jordao and Antonio Carlos Nazare Junior and Jessica Sena and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/Human-Activity-Recognition-based-on-Wearable-A-Benchmark.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {Arvix},
journal = {arXiv},
pages = {1-12},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Jordao, Artur; Kloss, Ricardo Barbosa; Schwartz, William Robson Latent hypernet: Exploring all Layers from Convolutional Neural Networks Inproceedings IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1-7, 2018. Links | BibTeX @inproceedings{Jordao:2018b:IJCNN,
title = {Latent hypernet: Exploring all Layers from Convolutional Neural Networks},
author = {Artur Jordao and Ricardo Barbosa Kloss and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Latent-HyperNet-Exploring-the-Layers.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {IEEE International Joint Conference on Neural Networks (IJCNN)},
pages = {1-7},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Jordao, Artur; Torres, Leonardo Antônio Borges; Schwartz, William Robson Novel Approaches to Human Activity Recognition based on Accelerometer Data Journal Article 12 (7), pp. 1387–1394, 2018. Links | BibTeX @article{Jordao:2018:SIVP,
title = {Novel Approaches to Human Activity Recognition based on Accelerometer Data},
author = {Artur Jordao and Leonardo Antônio Borges Torres and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Novel-Approaches-to-Human-Activity-Recognition-based-on.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {Signal, Image and Video Processing},
volume = {12},
number = {7},
pages = {1387–1394},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Kloss, Ricardo Barbosa; Jordao, Artur; Schwartz, William Robson Boosted Projection An Ensemble of Transformation Models Inproceedings 22nd Iberoamerican Congress on Pattern Recognition (CIARP), pp. 331-338, 2018. Links | BibTeX @inproceedings{Kloss:2018:CIARP,
title = {Boosted Projection An Ensemble of Transformation Models},
author = {Ricardo Barbosa Kloss and Artur Jordao and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Boosted-Projection-An-Ensemble-of-Transformation-Models.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {22nd Iberoamerican Congress on Pattern Recognition (CIARP)},
pages = {331-338},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Jordao, Artur; Kloss, Ricardo; Yamada, Fernando; Schwartz, William Robson Pruning Deep Neural Networks using Partial Least Squares Journal Article ArXiv e-prints, 2018. Links | BibTeX @article{Jordao:2018:arXivb,
title = {Pruning Deep Neural Networks using Partial Least Squares},
author = {Artur Jordao and Ricardo Kloss and Fernando Yamada and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/1810.07610.pdf},
year = {2018},
date = {2018-01-01},
journal = {ArXiv e-prints},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Jordao, Artur; Sena, Jessica; Schwartz, William Robson A Late Fusion Approach to Combine Multiple Pedestrian Detectors Inproceedings IAPR International Conference on Pattern Recognition (ICPR), pp. 1-6, 2016. Links | BibTeX @inproceedings{Correia:2016:ICPR,
title = {A Late Fusion Approach to Combine Multiple Pedestrian Detectors},
author = {Artur Jordao and Jessica Sena and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/A-Late-Fusion-Approach-to-Combine-Multiple.pdf},
year = {2016},
date = {2016-12-13},
booktitle = {IAPR International Conference on Pattern Recognition (ICPR)},
pages = {1-6},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Jordao, Artur; Schwartz, William Robson Oblique Random Forest based on Partial Least Squares Applied to Pedestrian Detection Inproceedings IEEE International Conference on Image Processing (ICIP), pp. 2931-2935, 2016. Links | BibTeX @inproceedings{Correia:2016:ICIP,
title = {Oblique Random Forest based on Partial Least Squares Applied to Pedestrian Detection},
author = {Artur Jordao and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/OBLIQUE-RANDOM-FOREST-BASED-ON-PARTIAL-LEAST-SQUARES-APPLIED-TO.pdf},
year = {2016},
date = {2016-09-25},
booktitle = {IEEE International Conference on Image Processing (ICIP)},
pages = {2931-2935},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Jordao, Artur The Good, the Fast and the Better Pedestrian Detector Masters Thesis Federal University of Minas Gerais, 2016. Resumo | Links | BibTeX @mastersthesis{Jordao:2016:MSc,
title = {The Good, the Fast and the Better Pedestrian Detector},
author = {Artur Jordao},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/dissertation_2016_ArturJordao.pdf},
year = {2016},
date = {2016-06-24},
school = {Federal University of Minas Gerais},
abstract = {Pedestrian detection is a well-known problem in Computer Vision, mostly because of its direct applications in surveillance, transit safety and robotics. In the past decade, several efforts have been performed to improve the detection in terms of accuracy, speed and feature enhancement. In this work, we propose and analyze techniques focusing on these points. First, we develop an accurate oblique random forest (oRF) associated with Partial Least Squares (PLS). The method utilizes the PLS to find a decision surface, at each node of a decision tree, that better splits the samples presented to it, based on some purity criterion. To measure the advantages provided by PLS on the oRF, we compare the proposed method with the oRF based on SVM. Second, we evaluate and compare filtering approaches to reduce the search space and keep only potential regions of interest to be presented to detectors, speeding up the detection process. Experimental results demonstrate that the evaluated filters are able to discard a large number of detection windows without compromising the accuracy. Finally, we propose a novel approach to extract powerful features regarding the scene. The method combines results of distinct pedestrian detectors by reinforcing the human hypothesis, whereas suppressing a significant number of false positives due to the lack of spatial consensus when multiple detectors are considered. Our proposed approach, referred to as Spatial Consensus (SC), outperforms all previously published state-of-the-art pedestrian detection methods.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Pedestrian detection is a well-known problem in Computer Vision, mostly because of its direct applications in surveillance, transit safety and robotics. In the past decade, several efforts have been performed to improve the detection in terms of accuracy, speed and feature enhancement. In this work, we propose and analyze techniques focusing on these points. First, we develop an accurate oblique random forest (oRF) associated with Partial Least Squares (PLS). The method utilizes the PLS to find a decision surface, at each node of a decision tree, that better splits the samples presented to it, based on some purity criterion. To measure the advantages provided by PLS on the oRF, we compare the proposed method with the oRF based on SVM. Second, we evaluate and compare filtering approaches to reduce the search space and keep only potential regions of interest to be presented to detectors, speeding up the detection process. Experimental results demonstrate that the evaluated filters are able to discard a large number of detection windows without compromising the accuracy. Finally, we propose a novel approach to extract powerful features regarding the scene. The method combines results of distinct pedestrian detectors by reinforcing the human hypothesis, whereas suppressing a significant number of false positives due to the lack of spatial consensus when multiple detectors are considered. Our proposed approach, referred to as Spatial Consensus (SC), outperforms all previously published state-of-the-art pedestrian detection methods. |