Zobrazeno 1 - 10
of 1 009
pro vyhledávání: '"Tony Martinez"'
Publikováno v:
Chemical Engineering Transactions, Vol 68 (2018)
Thames Water is the largest water company in the UK serving nine million customers with potable water and treating the wastewater from 15 million customers in London and the Thames Valley. Thames Water implements a range of practices to minimise odou
Externí odkaz:
https://doaj.org/article/49e3ea12c84747eca1556db2f68a8ac3
Autor:
Martinez, Tony
Publikováno v:
PGA Magazine (1044-1204); Feb2017, Vol. 98 Issue 2, p112-113, 2p
Publikováno v:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ad822dc8cfd1b7229a1dab2e878e9092
https://doi.org/10.1109/cvpr52688.2022.00085
https://doi.org/10.1109/cvpr52688.2022.00085
Autor:
Chris Tensmeyer, Tony Martinez
Publikováno v:
Pattern Recognition. 87:1-16
Identifying the type of a scanned form image greatly facilitates automated processing, including field segmentation and field recognition. Contrary to most prior work, we focus on unsupervised type identification, where the possible form types for a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1cb81ae2c8105f7bb7364eeacb181e22
https://doi.org/10.1016/j.patcog.2018.10.004
https://doi.org/10.1016/j.patcog.2018.10.004
Publikováno v:
Document Analysis and Recognition – ICDAR 2021 ISBN: 9783030863333
ICDAR (3)
ICDAR (3)
Stroke order and velocity are helpful features in the fields of signature verification, handwriting recognition, and handwriting synthesis. Recovering these features from offline handwritten text is a challenging and well-studied problem. We propose
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0c0813de892b762efe4869efa04624a
https://doi.org/10.1007/978-3-030-86334-0_27
https://doi.org/10.1007/978-3-030-86334-0_27
Publikováno v:
WACV
We tackle the challenging task of estimating global 3D joint locations for both hands via only monocular RGB input images. We propose a novel multi-stage convolutional neural network based pipeline that accurately segments and locates the hands despi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::342678a6a3e97a4b092f555ba865253d
http://arxiv.org/abs/2006.01320
http://arxiv.org/abs/2006.01320
Autor:
Chris Tensmeyer, Tony Martinez
Publikováno v:
SN Computer Science. 1
This review provides a comprehensive view of the field of historical document image binarization with a focus on the contributions made in the last decade. After the introduction of a standard benchmark dataset with the 2009 Document Image Binarizati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::88fe95bdd0515c2820853bbb5b1b6b8a
https://doi.org/10.1007/s42979-020-00176-1
https://doi.org/10.1007/s42979-020-00176-1
Publikováno v:
2020 IEEE Winter Conference on Applications of Computer Vision (WACV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f043bb69836e9e16b97033bd8a2ec1c0
https://doi.org/10.1109/wacv45572.2020.9093424
https://doi.org/10.1109/wacv45572.2020.9093424
Autor:
Chris Tensmeyer, Tony Martinez
Publikováno v:
WML@ICDAR
In keypoint regression, models are trained to consume an image and produce the x, y coordinates of some entity, e.g., a person's nose or corner of a document. Typical methods for doing so include linear regression over deep features to directly produ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d5ff450d45bedc8942fc9984b7e74198
https://doi.org/10.1109/icdarw.2019.40072
https://doi.org/10.1109/icdarw.2019.40072
Publikováno v:
ICDAR
One of the limitations for using Deep Learning models to solve binarization tasks is that there is a lack of large quantities of labeled data available to train such models. Efforts to create synthetic data for binarization mostly rely on heuristic i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::23f0136dc201dcd0308500679dee2617
https://doi.org/10.1109/icdar.2019.00036
https://doi.org/10.1109/icdar.2019.00036