Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Silva DA"'
Autor:
Silvia Silva da Costa Botelho, Joel De O. Gaya, Felipe Codevilla, Amanda Duarte, Paulo L. J. Drews-Jr
Publikováno v:
IEEE Computer Graphics and Applications. 39:71-83
This paper describes a method to restore degraded images captured in a participating media -- fog, turbid water, sand storm, etc. Differently from the related work that only deal with a medium, we obtain generality by using an image formation model a
Publikováno v:
LARS/SBR/WRE
Underwater image restoration represents a challenge to computer vision and image processing based on machine learning. Recent methodologies to tackle this problem are based on learning. However, the lack of paired image datasets leads the researches
Autor:
Lucas Ricardo Vieira Messias, Silvia Silva da Costa Botelho, Paulo L. J. Drews-Jr, Claudio D. MelloJr
Publikováno v:
Intelligent Systems ISBN: 9783030613761
BRACIS
BRACIS
The restoration of a corrupted image is a challenge to computer vision and image processing. In hazy, underwater and medical images, the lack of paired images lead the state of the art to synthesize datasets. The Generative Adversarial Networks (GANs
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::60917b2d7b6103eb0d659f101867c3db
https://doi.org/10.1007/978-3-030-61377-8_24
https://doi.org/10.1007/978-3-030-61377-8_24
Autor:
Paulo Drews, Valquiria Huttner, R. S. Guerra, Cristiano Rafael Steffens, Silvia Silva da Costa Botelho, Lucas Ricardo Vieira Messias
Publikováno v:
ICIP
Image restoration and image enhancement are critical image processing tasks since good image quality is mandatory for many image applications. We are particularly interested in the restoration of ill-exposed images. These effects are caused by sensor
Autor:
Humberto Bustince, Eduardo Nunes Borges, Giancarlo Lucca, Helida Santos, Jéssica C. S. Bueno, Graçaliz Pereira Dimuro, Camila Alves Dias, Silvia Silva da Costa Botelho, Paulo L. J. Drews-Jr, Eduardo Silva Palmeira
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783030219192
The data volume expansion has generated the need to develop efficient knowledge extraction techniques. Most problems that are processed by these techniques have complex information to be identified and use different machine learning methods, such as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0d89534a69630144b5eef3d48520fcb5
https://doi.org/10.1007/978-3-030-21920-8_21
https://doi.org/10.1007/978-3-030-21920-8_21
Publikováno v:
LARS/SBR
In this work, several state-of-the-art Blind Image Quality Assessment (IQA) metrics and measures are evaluated in order to verify how they behave on extreme conditions such as the ones found on pictures of metallic surfaces. This is an important rese
Publikováno v:
SIBGRAPI
Image dehazing can be described as the problem of mapping from a hazy image to a haze-free image. Most approaches to this problem use physical models based on simplifications and priors. In this work we demonstrate that a convolutional neural network
Autor:
Matheus M. dos Santos, Guilherme B. Zaffari, Silvia Silva da Costa Botelho, Daniel de A. Fernandes, Amanda Duarte
Publikováno v:
OCEANS 2016 - Shanghai.
This paper presents the bio-inspired underwater 3D SLAM algorithm called DolphinSLAM. First, every module of the DolphinSLAM algorithm is explained. Then, the effects of parameter variations regarding the parameters of the DolphinSLAM algorithm are i
Autor:
Tania Mezzadri Centeno, Willian Lautenschlger, Matheus Bacelo de Figueiredo, Silvia Silva da Costa Botelho, Mauricio M. Mata
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783540269724
IDEAL
IDEAL
In this paper we apply a Neural Network (NN) to reduce image dataset, distilling the massive datasets down to a new space of smaller dimension. Due to the possibility of these data have nonlinearities, traditional multivariate analysis, like the Prin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::96bce3f49c3b6be506f54c26ef3c36f0
https://doi.org/10.1007/11508069_17
https://doi.org/10.1007/11508069_17