Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Tomasz Malisiewicz"'
Publikováno v:
CVPR
This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points. Assignments are estimated by solving a differentiable optimal transport problem, whose co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2cd1f9cc32c38160edf4e5c8ed8c75f0
http://arxiv.org/abs/1911.11763
http://arxiv.org/abs/1911.11763
Publikováno v:
CVPR
ChArUco boards are used for camera calibration, monocular pose estimation, and pose verification in both robotics and augmented reality. Such fiducials are detectable via traditional computer vision methods (as found in OpenCV) in well-lit environmen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f8ca9cced950876bd5cc6d73c2103be3
https://doi.org/10.1109/cvpr.2019.00863
https://doi.org/10.1109/cvpr.2019.00863
The goal of this work is to find visually similar images even if they appear quite different at the raw pixel level. This task is particularly important for matching images across visual domains, such as photos taken over different seasons or lightin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c15c892c0e0c87555d75db81a4b55bf
Publikováno v:
CVPR Workshops
This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutiona
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f290bf464722682a0f1289d8a957dc0
http://arxiv.org/abs/1712.07629
http://arxiv.org/abs/1712.07629
Publikováno v:
ICCV
This paper focuses on the task of room layout estimation from a monocular RGB image. Prior works break the problem into two sub-tasks: semantic segmentation of floor, walls, ceiling to produce layout hypotheses, followed by an iterative optimization
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a979e0158419f4a0c5222ba0f40811ed
https://doi.org/10.1109/iccv.2017.521
https://doi.org/10.1109/iccv.2017.521
Publikováno v:
Springer US
We introduce algorithms to visualize feature spaces used by object detectors. Our method works by inverting a visual feature back to multiple natural images. We found that these visualizations allow us to analyze object detection systems in new ways
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c024795f370aafeae88f9ec9c2d1f784
Publikováno v:
ICPR
We present a non-linear object detector called Exemplar Network. Our model efficiently encodes the space of all possible mixture models, and offers a framework that generalizes recent exemplar-based object detection with monolithic detectors. We eval
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3b23e2dc0a180de36243c9abf6720530
https://doi.org/10.1109/icpr.2014.113
https://doi.org/10.1109/icpr.2014.113
Publikováno v:
ICCV
MIT web domain
MIT web domain
We introduce algorithms to visualize feature spaces used by object detectors. The tools in this paper allow a human to put on 'HOG goggles' and perceive the visual world as a HOG based object detector sees it. We found that these visualizations allow
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e546980973d9b24107afe0cc522f5203
https://doi.org/10.1109/iccv.2013.8
https://doi.org/10.1109/iccv.2013.8
Publikováno v:
Computer Vision – ECCV 2012 ISBN: 9783642337178
ECCV (1)
ECCV (1)
The presence of bias in existing object recognition datasets is now well-known in the computer vision community. While it remains in question whether creating an unbiased dataset is possible given limited resources, in this work we propose a discrimi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01ab93c0c309e8abf1a668c23fed4221
Publikováno v:
ICCV
This paper proposes a conceptually simple but surprisingly powerful method which combines the effectiveness of a discriminative object detector with the explicit correspondence offered by a nearest-neighbor approach. The method is based on training a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07e2ba649ed8ee9878dcc32d5fb195da
https://doi.org/10.1109/iccv.2011.6126229
https://doi.org/10.1109/iccv.2011.6126229