LCAD - Contribuições do(a) usuário(a) [pt-br]

Facial Expression Recognition with Convolutional Neural Networks: Coping with Few Data and the Training Sample Order

2018-01-16T02:27:32Z

Thiago Oliveira dos Santos:

[[category:Publicações]]
Journal Paper, Pattern Recognition International Journal, 2016

DOI: [http://dx.doi.org/10.1016/j.patcog.2016.07.026]

PDF: [http://www.sciencedirect.com/science/article/pii/S0031320316301753]

Accepted Manuscript: [https://www.researchgate.net/publication/305483977_Facial_Expression_Recognition_with_Convolutional_Neural_Networks_Coping_with_Few_Data_and_the_Training_Sample_Order]
== Authors ==
Andre Teixeira Lopes, Edilson de Aguiar, Alberto F. De Souza, Thiago Oliveira-Santos

== Abstract ==
Abstract Facial expression recognition has been an active research area in the past ten years, with growing application areas including avatar animation, neuromarketing and sociable robots. The recognition of facial expressions is not an easy problem for machine learning methods, since people can vary significantly in the way they show their expressions. Even images of the same person in the same facial expression can vary in brightness, background and pose, and these variations are emphasized if considering different subjects (because of variations in shape, ethnicity among others). Although facial expression recognition is very studied in the literature, few works perform fair evaluation avoiding mixing subjects while training and testing the proposed algorithms. Hence, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of Convolutional Neural Network and specific image pre-processing steps. Convolutional Neural Networks achieve better accuracy with big data. However, there are no publicly available datasets with sufficient data for facial expression recognition with deep architectures. Therefore, to tackle the problem, we apply some pre-processing techniques to extract only expression specific features from a face image and explore the presentation order of the samples during training. The experiments employed to evaluate our technique were carried out using three largely used public databases (CK+, \{JAFFE\} and BU-3DFE). A study of the impact of each image pre-processing operation in the accuracy rate is presented. The proposed method: achieves competitive results when compared with other facial expression recognition methods −96.76% of accuracy in the CK+ database - it is fast to train, and it allows for real time facial expression recognition with standard computers.

[http://www.lcad.inf.ufes.br/~alopes/papers/patcog-si-deep-image-2016/graphical-abstract.png Graphical Abstract]

== BibTeX ==

@article{Lopes2016,
title = "Facial Expression Recognition with Convolutional Neural Networks: Coping with Few Data and the Training Sample Order ",
journal = "Pattern Recognition ",
year = "2016",
issn = "0031-3203",
doi = "http://dx.doi.org/10.1016/j.patcog.2016.07.026",
url = "http://www.sciencedirect.com/science/article/pii/S0031320316301753",
author = "Andre Teixeira Lopes and Edilson de Aguiar and Alberto F. De Souza and Thiago Oliveira-Santos"
}

== Code ==
github: [https://github.com/andreteixeiralopes/deepfacialexpression]

Facial Expression Recognition with Convolutional Neural Networks: Coping with Few Data and the Training Sample Order

2018-01-16T02:19:16Z

Thiago Oliveira dos Santos:

[[category:Publicações]]
Journal Paper, Pattern Recognition International Journal, 2016

DOI: [http://dx.doi.org/10.1016/j.patcog.2016.07.026]

PDF: [http://www.sciencedirect.com/science/article/pii/S0031320316301753]

Accepted Manuscript: [http://www.lcad.inf.ufes.br/~alopes/papers/patcog-si-deep-image-2016/accepted-manuscript.pdf]
== Authors ==
Andre Teixeira Lopes, Edilson de Aguiar, Alberto F. De Souza, Thiago Oliveira-Santos

== Abstract ==
Abstract Facial expression recognition has been an active research area in the past ten years, with growing application areas including avatar animation, neuromarketing and sociable robots. The recognition of facial expressions is not an easy problem for machine learning methods, since people can vary significantly in the way they show their expressions. Even images of the same person in the same facial expression can vary in brightness, background and pose, and these variations are emphasized if considering different subjects (because of variations in shape, ethnicity among others). Although facial expression recognition is very studied in the literature, few works perform fair evaluation avoiding mixing subjects while training and testing the proposed algorithms. Hence, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of Convolutional Neural Network and specific image pre-processing steps. Convolutional Neural Networks achieve better accuracy with big data. However, there are no publicly available datasets with sufficient data for facial expression recognition with deep architectures. Therefore, to tackle the problem, we apply some pre-processing techniques to extract only expression specific features from a face image and explore the presentation order of the samples during training. The experiments employed to evaluate our technique were carried out using three largely used public databases (CK+, \{JAFFE\} and BU-3DFE). A study of the impact of each image pre-processing operation in the accuracy rate is presented. The proposed method: achieves competitive results when compared with other facial expression recognition methods −96.76% of accuracy in the CK+ database - it is fast to train, and it allows for real time facial expression recognition with standard computers.

[http://www.lcad.inf.ufes.br/~alopes/papers/patcog-si-deep-image-2016/graphical-abstract.png Graphical Abstract]

== BibTeX ==

@article{Lopes2016,
title = "Facial Expression Recognition with Convolutional Neural Networks: Coping with Few Data and the Training Sample Order ",
journal = "Pattern Recognition ",
year = "2016",
issn = "0031-3203",
doi = "http://dx.doi.org/10.1016/j.patcog.2016.07.026",
url = "http://www.sciencedirect.com/science/article/pii/S0031320316301753",
author = "Andre Teixeira Lopes and Edilson de Aguiar and Alberto F. De Souza and Thiago Oliveira-Santos"
}

== Code ==
github: [https://github.com/andreteixeiralopes/deepfacialexpression]

A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model

2015-10-22T16:57:33Z

Thiago Oliveira dos Santos: /* Video */

Conference paper, SIBGRAPI, 2015, PDF: [http://sibgrapi.sid.inpe.br/col/sid.inpe.br/sibgrapi/2015/06.10.13.46/doc/PID3755287.pdf]

== Authors ==
Rodrigo Berriel, Edilson de Aguiar, Vanderlei Vieira de Souza Filho, Thiago Oliveira-Santos

== Abstract ==
In this paper we present a particle filter-based lane marker tracking approach using a cubic spline model. The system can detect the two main lane markers (i.e. lane strips) of marked roads from a monocular camera mounted on the top of a vehicle. Traditional lane marker detection and tracking systems have limitations to properly detect curved roads and to use temporal information to better estimate and track lane markers. The proposed system works on a temporal sequence of images. For each image, one at time, it applies a sequence of steps comprising an inverse perspective mapping to correct for perspective distortions, and a particle filter to smoothly track the lane markers along time. The output of the system is a lane marker generated by a cubic spline interpolation scheme to fit a wider range of lanes. Our system can run in real applications and it was validated with various road and traffic conditions. As a result, it achieves a high precision (98.13%) and a small error (0.0143 meters).

[[imagem:Resumo_A_Particle_Filter-based_Lane_Marker_Tracking_Approach_using_a_Cubic_Spline_Model.jpg]]

== Video ==
[https://youtu.be/cI79RZhIDCI Demo]

A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model

2015-10-22T16:56:31Z

Thiago Oliveira dos Santos: /* Abstract */

A Facial Expression Recognition System Using Convolutional Networks

2015-10-22T16:53:32Z

Thiago Oliveira dos Santos:

Conference paper, SIBGRAPI, 2015, PDF: [http://sibgrapi.sid.inpe.br/col/sid.inpe.br/sibgrapi/2015/06.19.21.39/doc/PID3755347.pdf]

== Authors ==
Andre Teixeira Lopes, Edilson de Aguiar, Thiago Oliveira-Santos

== Abstract ==
Facial expression recognition has been an active research area in the past ten years, with a growing application area like avatar animation and neuromarketing. The recognition of facial expressions is not an easy problem for machine learning methods, since different people can vary in the way that they show their expressions. And even an image of the same person in one expression can vary in brightness, background and position. Therefore, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of standard methods, like Convolutional Network and specific image pre-processing steps. Convolutional networks, and the most machine learning methods, achieve better accuracy depending on a given feature set. Therefore, a study of some image pre-processing operations that extract only expression specific
features of a face image is also presented. The experiments were carried out using a largely used public database for this problem. A study of the impact of each image pre-processing operation in the accuracy rate is presented. To the best of our knowledge, our method achieves the best result in the literature, 97.81% of accuracy, and takes less time to train than state-of-the-art methods.

[[Arquivo:Resumo_A_Facial_Expression_Recognition_System_Using_Convolutional_Networks.jpg]]

A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model

2015-10-22T16:53:17Z

Thiago Oliveira dos Santos:

Publicações do LCAD

2015-10-22T16:52:07Z

Thiago Oliveira dos Santos: /* Papers Summaries */

[[category:Publicações]]
== [http://www.lcad.inf.ufes.br/~alberto/pesquisadores-lcad-producao Publicações do LCAD desde 1996] ==

== Papers Summaries ==

[[A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model]], 2015, SIBGRAPI

[[A Facial Expression Recognition System Using Convolutional Networks]], 2015, SIBGRAPI

Publicações do LCAD

2015-10-22T16:51:33Z

Thiago Oliveira dos Santos: /* Papers Summaries */

[[category:Publicações]]
== [http://www.lcad.inf.ufes.br/~alberto/pesquisadores-lcad-producao Publicações do LCAD desde 1996] ==

== Papers Summaries ==

[[A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model, 2015, SIBGRAPI]]

[[A Facial Expression Recognition System Using Convolutional Networks, 2015, SIBGRAPI]]

A Facial Expression Recognition System Using Convolutional Networks

2015-10-22T16:50:58Z

Thiago Oliveira dos Santos: /* Institutions */

PDF: [http://sibgrapi.sid.inpe.br/col/sid.inpe.br/sibgrapi/2015/06.19.21.39/doc/PID3755347.pdf]

== Authors ==
Andre Teixeira Lopes, Edilson de Aguiar, Thiago Oliveira-Santos

== Abstract ==
Facial expression recognition has been an active research area in the past ten years, with a growing application area like avatar animation and neuromarketing. The recognition of facial expressions is not an easy problem for machine learning methods, since different people can vary in the way that they show their expressions. And even an image of the same person in one expression can vary in brightness, background and position. Therefore, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of standard methods, like Convolutional Network and specific image pre-processing steps. Convolutional networks, and the most machine learning methods, achieve better accuracy depending on a given feature set. Therefore, a study of some image pre-processing operations that extract only expression specific
features of a face image is also presented. The experiments were carried out using a largely used public database for this problem. A study of the impact of each image pre-processing operation in the accuracy rate is presented. To the best of our knowledge, our method achieves the best result in the literature, 97.81% of accuracy, and takes less time to train than state-of-the-art methods.

[[Arquivo:Resumo_A_Facial_Expression_Recognition_System_Using_Convolutional_Networks.jpg]]

A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model

2015-10-22T16:50:50Z

Thiago Oliveira dos Santos: /* Institutions */

PDF: [http://sibgrapi.sid.inpe.br/col/sid.inpe.br/sibgrapi/2015/06.10.13.46/doc/PID3755287.pdf]

== Authors ==
Rodrigo Berriel, Edilson de Aguiar, Vanderlei Vieira de Souza Filho, Thiago Oliveira-Santos

== Abstract ==
In this paper we present a particle filter-based lane marker tracking approach using a cubic spline model. The system can detect the two main lane markers (i.e. lane strips) of marked roads from a monocular camera mounted on the top of a vehicle. Traditional lane marker detection and tracking systems have limitations to properly detect curved roads and to use temporal information to better estimate and track lane markers. The proposed system works on a temporal sequence of images. For each image, one at time, it applies a sequence of steps comprising an inverse perspective mapping to correct for perspective distortions, and a particle filter to smoothly track the lane markers along time. The output of the system is a lane marker generated by a cubic spline interpolation scheme to fit a wider range of lanes. Our system can run in real applications and it was validated with various road and traffic conditions. As a result, it achieves a high precision (98.13%) and a small error (0.0143 meters).

[[imagem:Resumo_A_Particle_Filter-based_Lane_Marker_Tracking_Approach_using_a_Cubic_Spline_Model.jpg]]

A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model

2015-10-22T16:49:57Z

Thiago Oliveira dos Santos: /* Authors */

PDF: [http://sibgrapi.sid.inpe.br/col/sid.inpe.br/sibgrapi/2015/06.10.13.46/doc/PID3755287.pdf]

== Authors ==
Rodrigo Berriel, Edilson de Aguiar, Vanderlei Vieira de Souza Filho, Thiago Oliveira-Santos

== Institutions ==
Federal University of Espírito Santo, Brazil

== Abstract ==
In this paper we present a particle filter-based lane marker tracking approach using a cubic spline model. The system can detect the two main lane markers (i.e. lane strips) of marked roads from a monocular camera mounted on the top of a vehicle. Traditional lane marker detection and tracking systems have limitations to properly detect curved roads and to use temporal information to better estimate and track lane markers. The proposed system works on a temporal sequence of images. For each image, one at time, it applies a sequence of steps comprising an inverse perspective mapping to correct for perspective distortions, and a particle filter to smoothly track the lane markers along time. The output of the system is a lane marker generated by a cubic spline interpolation scheme to fit a wider range of lanes. Our system can run in real applications and it was validated with various road and traffic conditions. As a result, it achieves a high precision (98.13%) and a small error (0.0143 meters).

[[imagem:Resumo_A_Particle_Filter-based_Lane_Marker_Tracking_Approach_using_a_Cubic_Spline_Model.jpg]]

A Facial Expression Recognition System Using Convolutional Networks

2015-10-22T16:49:06Z

Thiago Oliveira dos Santos: /* Authors */

PDF: [http://sibgrapi.sid.inpe.br/col/sid.inpe.br/sibgrapi/2015/06.19.21.39/doc/PID3755347.pdf]

== Authors ==
Andre Teixeira Lopes, Edilson de Aguiar, Thiago Oliveira-Santos

== Institutions ==
Federal University of Espirito Santo, Brazil

== Abstract ==
Facial expression recognition has been an active research area in the past ten years, with a growing application area like avatar animation and neuromarketing. The recognition of facial expressions is not an easy problem for machine learning methods, since different people can vary in the way that they show their expressions. And even an image of the same person in one expression can vary in brightness, background and position. Therefore, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of standard methods, like Convolutional Network and specific image pre-processing steps. Convolutional networks, and the most machine learning methods, achieve better accuracy depending on a given feature set. Therefore, a study of some image pre-processing operations that extract only expression specific
features of a face image is also presented. The experiments were carried out using a largely used public database for this problem. A study of the impact of each image pre-processing operation in the accuracy rate is presented. To the best of our knowledge, our method achieves the best result in the literature, 97.81% of accuracy, and takes less time to train than state-of-the-art methods.

[[Arquivo:Resumo_A_Facial_Expression_Recognition_System_Using_Convolutional_Networks.jpg]]

Arquivo:Resumo A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model.jpg

2015-10-22T16:47:35Z

Thiago Oliveira dos Santos: Overview of our lane marker tracking system: from a sequence of frames (a), the Inverse Perspective Mapping is applied (b) and the lane marker is detected and tracked with the use of a particle filter (c). The output (d) are tracked lane markers modele...

Overview of our lane marker tracking system: from a sequence of frames (a), the Inverse Perspective Mapping is applied (b) and the lane marker is detected and tracked with the use of a particle filter (c). The output (d) are tracked lane markers modeled by cubic splines for each side. Yellow splines represent all the particles and the red one represents the output, a virtual best particle. The system reports an error of 0.0143 meters with 98.13% of precision.

Arquivo:Resumo.jpg

2015-10-22T16:47:22Z

Thiago Oliveira dos Santos: Thiago Oliveira dos Santos foi enviada uma nova versão de "Arquivo:Resumo.jpg": Nao consegui deletar

Arquivo:Resumo A Facial Expression Recognition System Using Convolutional Networks.jpg

2015-10-22T16:41:31Z

Thiago Oliveira dos Santos: This work proposes a simple solution for facial expression recognition that uses a combination of standard methods, like Convolutional Network and specific image pre-processing steps. To the best of our knowledge, our method achieves the best result in...

This work proposes a simple solution for facial expression recognition that uses a combination of standard methods, like Convolutional Network and specific image pre-processing steps. To the best of our knowledge, our method achieves the best result in the literature, 97.81% of accuracy, and takes less time to train than state-of-the-art methods.

A Facial Expression Recognition System Using Convolutional Networks

2015-10-22T16:40:55Z

Thiago Oliveira dos Santos: /* Abstract */

== Authors ==
Andre Teixeira Lopes, Edilson de Aguiar, Thiago Oliveira-Santos

== Institutions ==
Federal University of Espirito Santo, Brazil

== Abstract ==
Facial expression recognition has been an active research area in the past ten years, with a growing application area like avatar animation and neuromarketing. The recognition of facial expressions is not an easy problem for machine learning methods, since different people can vary in the way that they show their expressions. And even an image of the same person in one expression can vary in brightness, background and position. Therefore, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of standard methods, like Convolutional Network and specific image pre-processing steps. Convolutional networks, and the most machine learning methods, achieve better accuracy depending on a given feature set. Therefore, a study of some image pre-processing operations that extract only expression specific
features of a face image is also presented. The experiments were carried out using a largely used public database for this problem. A study of the impact of each image pre-processing operation in the accuracy rate is presented. To the best of our knowledge, our method achieves the best result in the literature, 97.81% of accuracy, and takes less time to train than state-of-the-art methods.

[[Arquivo:Resumo_A_Facial_Expression_Recognition_System_Using_Convolutional_Networks.jpg]]

A Facial Expression Recognition System Using Convolutional Networks

2015-10-22T16:40:45Z

Thiago Oliveira dos Santos: /* Abstract */

== Authors ==
Andre Teixeira Lopes, Edilson de Aguiar, Thiago Oliveira-Santos

== Institutions ==
Federal University of Espirito Santo, Brazil

== Abstract ==
Facial expression recognition has been an active research area in the past ten years, with a growing application area like avatar animation and neuromarketing. The recognition of facial expressions is not an easy problem for machine learning methods, since different people can vary in the way that they show their expressions. And even an image of the same person in one expression can vary in brightness, background and position. Therefore, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of standard methods, like Convolutional Network and specific image pre-processing steps. Convolutional networks, and the most machine learning methods, achieve better accuracy depending on a given feature set. Therefore, a study of some image pre-processing operations that extract only expression specific
features of a face image is also presented. The experiments were carried out using a largely used public database for this problem. A study of the impact of each image pre-processing operation in the accuracy rate is presented. To the best of our knowledge, our method achieves the best result in the literature, 97.81% of accuracy, and takes less time to train than state-of-the-art methods.
[[Arquivo:Resumo_A_Facial_Expression_Recognition_System_Using_Convolutional_Networks.jpg]]

A Facial Expression Recognition System Using Convolutional Networks

2015-10-22T16:39:42Z

Thiago Oliveira dos Santos: /* Abstract */

Arquivo:Resumo.jpg

2015-10-22T16:35:52Z

Thiago Oliveira dos Santos: Limpou toda a página

A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model

2015-10-22T16:34:21Z

Thiago Oliveira dos Santos: /* Abstract */

== Authors ==
Rodrigo Berriel, Edilson de Aguiar, Vanderlei Vieira de Souza Filho, Thiago Oliveira-Santos

== Institutions ==
Federal University of Espírito Santo, Brazil

== Abstract ==
In this paper we present a particle filter-based lane marker tracking approach using a cubic spline model. The system can detect the two main lane markers (i.e. lane strips) of marked roads from a monocular camera mounted on the top of a vehicle. Traditional lane marker detection and tracking systems have limitations to properly detect curved roads and to use temporal information to better estimate and track lane markers. The proposed system works on a temporal sequence of images. For each image, one at time, it applies a sequence of steps comprising an inverse perspective mapping to correct for perspective distortions, and a particle filter to smoothly track the lane markers along time. The output of the system is a lane marker generated by a cubic spline interpolation scheme to fit a wider range of lanes. Our system can run in real applications and it was validated with various road and traffic conditions. As a result, it achieves a high precision (98.13%) and a small error (0.0143 meters).

[[imagem:Resumo_A_Particle_Filter-based_Lane_Marker_Tracking_Approach_using_a_Cubic_Spline_Model.jpg]]

A Facial Expression Recognition System Using Convolutional Networks

2015-10-22T16:32:09Z

Thiago Oliveira dos Santos: Criou página com ' == Authors == Andre Teixeira Lopes, Edilson de Aguiar, Thiago Oliveira-Santos == Institutions == Federal University of Espirito Santo, Brazil == Abstract == Facial expressi...'

A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model

2015-10-22T16:27:46Z

Thiago Oliveira dos Santos: /* Institutions */

Arquivo:Resumo.jpg

2015-10-22T16:26:50Z

A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model

2015-10-22T16:19:48Z

Thiago Oliveira dos Santos:

== Authors ==
Rodrigo Berriel, Edilson de Aguiar, Vanderlei Vieira de Souza Filho, Thiago Oliveira-Santos

== Institutions ==
Federal University of Esp´ırito Santo, Brazil

== Abstract ==
In this paper we present a particle filter-based lane marker tracking approach using a cubic spline model. The system can detect the two main lane markers (i.e. lane strips) of marked roads from a monocular camera mounted on the top of a vehicle. Traditional lane marker detection and tracking systems have limitations to properly detect curved roads and to use temporal information to better estimate and track lane markers. The proposed system works on a temporal sequence of images. For each image, one at time, it applies a sequence of steps comprising an inverse perspective mapping to correct for perspective distortions, and a particle filter to smoothly track the lane markers along time. The output of the system is a lane marker generated by a cubic spline interpolation scheme to fit a wider range of lanes. Our system can run in real applications and it was validated with various road and traffic conditions. As a result, it achieves a high precision (98.13%) and a small error (0.0143 meters).

[[imagem:Resumo.jpg]]

A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model

2015-10-22T16:18:58Z

Thiago Oliveira dos Santos: Criou página com '== Authors == Rodrigo Berriel, Edilson de Aguiar, Vanderlei Vieira de Souza Filho, Thiago Oliveira-Santos == Institutions == Federal University of Esp´ırito Santo, Brazil ...'

Publicações do LCAD

2015-10-22T16:16:22Z

Thiago Oliveira dos Santos: /* Papers Summaries */

Publicações do LCAD

2015-10-22T16:15:54Z

Thiago Oliveira dos Santos: /* Papers Summaries */

[[category:Publicações]]
== [http://www.lcad.inf.ufes.br/~alberto/pesquisadores-lcad-producao Publicações do LCAD desde 1996] ==

== Papers Summaries ==

[[A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model]], 2015, SIBGRAPI
[[A Facial Expression Recognition System Using Convolutional Networks]], 2015, SIBGRAPI

Publicações do LCAD

2015-10-22T16:15:26Z

Thiago Oliveira dos Santos: /* A Facial Expression Recognition System Using Convolutional Networks */

Publicações do LCAD

2015-10-22T16:15:18Z

Thiago Oliveira dos Santos: /* Papers Summaries */

[[category:Publicações]]
== [http://www.lcad.inf.ufes.br/~alberto/pesquisadores-lcad-producao Publicações do LCAD desde 1996] ==

== Papers Summaries ==

[[A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model]], 2015, SIBGRAPI

== [[A Facial Expression Recognition System Using Convolutional Networks]] ==
2015, SIBGRAPI

Publicações do LCAD

2015-10-22T16:14:20Z

Thiago Oliveira dos Santos: /* A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model */

[[category:Publicações]]
== [http://www.lcad.inf.ufes.br/~alberto/pesquisadores-lcad-producao Publicações do LCAD desde 1996] ==

== Papers Summaries ==

== [[A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model]] == 2015, SIBGRAPI

== [[A Facial Expression Recognition System Using Convolutional Networks]] ==
2015, SIBGRAPI

Publicações do LCAD

2015-10-22T16:13:35Z

Thiago Oliveira dos Santos: /* A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model */

[[category:Publicações]]
== [http://www.lcad.inf.ufes.br/~alberto/pesquisadores-lcad-producao Publicações do LCAD desde 1996] ==

== Papers Summaries ==

== [[A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model]] ==
2015, SIBGRAPI
== [[A Facial Expression Recognition System Using Convolutional Networks]] ==
2015, SIBGRAPI

Publicações do LCAD

2015-10-22T16:13:02Z

Thiago Oliveira dos Santos: /* Papers Summaries */

Publicações do LCAD

2015-10-22T16:12:52Z

Thiago Oliveira dos Santos: /* Test 1 */

[[category:Publicações]]
== [http://www.lcad.inf.ufes.br/~alberto/pesquisadores-lcad-producao Publicações do LCAD desde 1996] ==

== Papers Summaries ==
== [[A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model]] ==
2015, SIBGRAPI

Publicações do LCAD

2015-10-22T16:10:04Z

Thiago Oliveira dos Santos: /* Test 1 */

[[category:Publicações]]
== [http://www.lcad.inf.ufes.br/~alberto/pesquisadores-lcad-producao Publicações do LCAD desde 1996] ==

== Papers Summaries ==
== [[Test 1]] ==
[[Arquivo:Exemplo.jpg]]

Test 1

2015-10-22T16:06:42Z

Thiago Oliveira dos Santos: /* == Texto do cabeçalho == */

Ola esse eh um texto de teste!

== Cabecalho 1 ==
TEsete denovo!
== Cabecallho 2 ==

Test 1

2015-10-22T16:06:14Z

Thiago Oliveira dos Santos: Criou página com 'Ola esse eh um texto de teste! == Cabecalho 1 == TEsete denovo! == == Texto do cabeçalho == == == Cabecallho 2 =='

Ola esse eh um texto de teste!

== Cabecalho 1 ==
TEsete denovo!
== == Texto do cabeçalho == ==

== Cabecallho 2 ==

Publicações do LCAD

2015-10-22T16:04:37Z

Thiago Oliveira dos Santos: /* Papers Summaries */

[[category:Publicações]]
== [http://www.lcad.inf.ufes.br/~alberto/pesquisadores-lcad-producao Publicações do LCAD desde 1996] ==

== Papers Summaries ==
== [[Test 1]] ==