Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Potje, Guilherme"'
Autor:
Cadar, Felipe, Potje, Guilherme, Martins, Renato, Demonceaux, Cédric, Nascimento, Erickson R.
Visual correspondence is a crucial step in key computer vision tasks, including camera localization, image registration, and structure from motion. The most effective techniques for matching keypoints currently involve using learned sparse or dense m
Externí odkaz:
http://arxiv.org/abs/2410.09533
We introduce a lightweight and accurate architecture for resource-efficient visual correspondence. Our method, dubbed XFeat (Accelerated Features), revisits fundamental design choices in convolutional neural networks for detecting, extracting, and ma
Externí odkaz:
http://arxiv.org/abs/2404.19174
Autor:
Cadar, Felipe, Melo, Welerson, Kanagasabapathi, Vaishnavi, Potje, Guilherme, Martins, Renato, Nascimento, Erickson R.
Publikováno v:
Pattern Recognition Letters 2023
We propose a novel learned keypoint detection method to increase the number of correct matches for the task of non-rigid image correspondence. By leveraging true correspondences acquired by matching annotated image pairs with a specified descriptor e
Externí odkaz:
http://arxiv.org/abs/2309.00434
Local feature extraction is a standard approach in computer vision for tackling important tasks such as image matching and retrieval. The core assumption of most methods is that images undergo affine transformations, disregarding more complicated eff
Externí odkaz:
http://arxiv.org/abs/2304.00583
We present a novel learned keypoint detection method designed to maximize the number of correct matches for the task of non-rigid image correspondence. Our training framework uses true correspondences, obtained by matching annotated image pairs with
Externí odkaz:
http://arxiv.org/abs/2212.09589
Most of the existing handcrafted and learning-based local descriptors are still at best approximately invariant to affine image transformations, often disregarding deformable surfaces. In this paper, we take one step further by proposing a new approa
Externí odkaz:
http://arxiv.org/abs/2203.12016
Despite the advances in extracting local features achieved by handcrafted and learning-based descriptors, they are still limited by the lack of invariance to non-rigid transformations. In this paper, we present a new approach to compute features from
Externí odkaz:
http://arxiv.org/abs/2111.10617
Autor:
Potje, Guilherme1 guipotje@dcc.ufmg.br, Resende, Gabriel1 gabrieldr@dcc.ufmg.br, Campos, Mario1 mario@dcc.ufmg.br, Nascimento, Erickson1 erickson@dcc.ufmg.br
Publikováno v:
Machine Vision & Applications. Nov2017, Vol. 28 Issue 8, p937-952. 16p.
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Akademický článek
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