@InProceedings{CruzCaSaPeLeCl:2017:ImAcAu,
author = "Cruz, Rommel Anatoli Quintanilla and Cacau, Diego Carri{\c{c}}o
and Santos, Renato Moraes dos and Pereira, Evandro Jose Ribeiro
and Leta, Fabiana and Clua, Esteban",
affiliation = "{Universidade Federal Fluminense} and {Universidade Federal
Fluminense} and {LMDC - Universidade Federal Fluminense} and {LMDC
- Universidade Federal Fluminense} and {LMDC - Universidade
Federal Fluminense} and {Universidade Federal Fluminense}",
title = "Improving accuracy of automatic fracture detection in borehole
images with deep learning and GPUs",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Deep learning, Automatic fracture detection, Pattern
recognition.",
abstract = "The logging and further analysis of borehole images is a major
step in the interpretation of geological events. Natural fractures
and beddings are features whose identification is commonly
performed using acoustic and electrical borehole imaging tools.
Such identification is a tedious task and is made visually by
geologists, who must be experts on classification. The correct
identification of planar features, represented as sinusoids into
an image projection, depends on the quality of the images. Due to
the distortions and noises of the images, known as artifacts, the
automatic features detection is not trivial through conventional
image processing methods. Since the identification process has to
ensure that the marked events are true with minimal
inconsistencies, we propose a pioneering approach to improving the
quality of the results by applying deep neural networks to confirm
or exclude candidate features extracted by a regular Hough
transform. This is the first approach in literature to improve the
quality of geological auto-detected marks by applying deep
learning techniques for borehole images where our implementation
is able to exclude most of the false positive marks.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
doi = "10.1109/SIBGRAPI.2017.52",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.52",
language = "en",
ibi = "8JMKD3MGPAW/3PFRL22",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFRL22",
targetfile = "PID4960365.pdf",
urlaccessdate = "2024, Apr. 27"
}