no code implementations • 2 Apr 2017 • Fabio Dittrich, Luiz E. S. de Oliveira, Alceu S. Britto Jr., Alessandro L. Koerich
For such an aim, corner points are extracted from groups of people in a foreground image and computed by a learning algorithm which estimates the number of people in the scene.
no code implementations • 16 Apr 2019 • Jonathan de Matos, Alceu de S. Britto Jr., Luiz E. S. Oliveira, Alessandro L. Koerich
This work proposes a classification approach for breast cancer histopathologic images (HI) that uses transfer learning to extract features from HI using an Inception-v3 CNN pre-trained with ImageNet dataset.
no code implementations • 16 Apr 2019 • Jonathan de Matos, Alceu de Souza Britto Jr., Luiz E. S. Oliveira, Alessandro L. Koerich
In this work we present a literature review about the computing techniques to process HI, including shallow and deep methods.
no code implementations • 28 May 2019 • Dylan C. Tannugi, Alceu S. Britto Jr., Alessandro L. Koerich
A common way to circumvent the lack of data is to use CNNs trained on large datasets of different domains and fine-tuning the layers of such networks to the target domain.
Facial Expression Recognition Facial Expression Recognition (FER)
no code implementations • 28 May 2019 • Jonathan de Matos, Alceu de S. Britto Jr., Luiz E. S. de Oliveira, Alessandro L. Koerich
Biopsies are the gold standard for breast cancer diagnosis.
no code implementations • 28 May 2019 • Daniel Vriesman, Alessandro Zimmer, Alceu S. Britto Jr., Alessandro L. Koerich
In this paper, the concept of representation learning based on deep neural networks is applied as an alternative to the use of handcrafted features in a method for automatic visual inspection of corroded thermoelectric metallic pipes.
no code implementations • 22 Jun 2019 • Kelly L. Wiggers, Alceu S. Britto Jr., Laurent Heutte, Alessandro L. Koerich, Luiz S. Oliveira
This paper presents a novel approach for image retrieval and pattern spotting in document image collections.
no code implementations • 25 Jun 2019 • Juan D. S. Ortega, Patrick Cardinal, Alessandro L. Koerich
In this paper we propose a fusion approach to continuous emotion recognition that combines visual and auditory modalities in their representation spaces to predict the arousal and valence levels.
no code implementations • 6 Jul 2019 • Juan D. S. Ortega, Mohammed Senoussaoui, Eric Granger, Marco Pedersoli, Patrick Cardinal, Alessandro L. Koerich
This paper presents a novel deep neural network (DNN) for multimodal fusion of audio, video and text modalities for emotion recognition.
no code implementations • 16 Jul 2019 • Israel A. Laurensi R., Luciana T. Menon, Manoel Camillo O. Penna N., Alessandro L. Koerich, Alceu S. Britto Jr
This paper proposes a face anti-spoofing user-centered model (FAS-UCM).
no code implementations • 4 Sep 2019 • Bernardo B. Gatto, Eulanda M. dos Santos, Alessandro L. Koerich, Kazuhiro Fukui, Waldir S. S. Junior
In this paper, we present a new method for multi-dimensional data classification that relies on two premises: 1) multi-dimensional data are usually represented by tensors, since this brings benefits from multilinear algebra and established tensor factorization methods; and 2) multilinear data can be described by a subspace of a vector space.
no code implementations • 31 Jan 2020 • Sevegni Odilon Clement Allognon, Alessandro L. Koerich, Alceu de S. Britto Jr
In this paper, we present a new model for continuous emotion recognition based on facial expression recognition by using an unsupervised learning approach based on transfer learning and autoencoders.
no code implementations • 31 Jan 2020 • Steve Tsham Mpinda Ataky, Jonathan de Matos, Alceu de S. Britto Jr., Luiz E. S. Oliveira, Alessandro L. Koerich
Such a problem is troublesome because most of the ML algorithms attempt to optimize a loss function that does not take into account the data imbalance.
no code implementations • 18 May 2020 • Voncarlos M. Araujo, Alceu S. Britto Jr., Luiz E. S. Oliveira, Alessandro L. Koerich
Automatic plant classification is a challenging problem due to the wide biodiversity of the existing plant species in a fine-grained scenario.
no code implementations • 18 Mar 2021 • Bernardo B. Gatto, Juan G. Colonna, Eulanda M. dos Santos, Alessandro L. Koerich, Kazuhiro Fukui
Automatic analysis of bioacoustic signals is a fundamental tool to evaluate the vitality of our planet.
no code implementations • 21 Apr 2022 • Steve T. M. Ataky, Diego Saqui, Jonathan de Matos, Alceu S. Britto Jr., Alessandro L. Koerich
Image pyramid multiresolution representations are a useful data structure for image analysis and manipulation over a spectrum of spatial scales.
no code implementations • 26 Apr 2022 • Bruna Delazeri, Leonardo L. Veras, Alceu de S. Britto Jr., Jean Paul Barddal, Alessandro L. Koerich
This work describes different strategies to generate unsupervised representations obtained through the concept of self-taught learning for facial emotion recognition (FER).
no code implementations • 4 Aug 2022 • Caio da S. Dias, Alceu de S. Britto Jr., Jean P. Barddal, Laurent Heutte, Alessandro L. Koerich
This paper presents a deep learning approach for image retrieval and pattern spotting in digital collections of historical documents.
no code implementations • Expert Systems with Applications 2015 • Paulo R.L. de Almeida, Luiz S. Oliveira, Alceu S. Britto Jr., Eunelson J. Silva Jr., Alessandro L. Koerich
To mitigate this difficulty, in this paper we introduce a new parking lot dataset composed of 695, 899 images captured from two parking lots with three different camera views.
1 code implementation • 1 Jul 2020 • Thiago M. Paixão, Rodrigo F. Berriel, Maria C. S. Boeres, Alessandro L. Koerich, Claudine Badue, Alberto F. de Souza, Thiago Oliveira-Santos
The solution presented in this work extends our previous deep learning method for single-page reconstruction to a more realistic/complex scenario: the reconstruction of several mixed shredded documents at once.
1 code implementation • arXiv preprint 2019 • Sajjad Abdoli, Luiz G. Hafemann, Jerome Rony, Ismail Ben Ayed, Patrick Cardinal, Alessandro L. Koerich
We demonstrate the existence of universal adversarial perturbations, which can fool a family of audio classification architectures, for both targeted and untargeted attack scenarios.