@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}