Zobrazeno 1 - 10
of 157
pro vyhledávání: '"Nascimento, Erickson"'
Supervised machine learning methods require large-scale training datasets to perform well in practice. Synthetic data has been showing great progress recently and has been used as a complement to real data. However, there is yet a great urge to asses
Externí odkaz:
http://arxiv.org/abs/2412.05466
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
Translating written sentences from oral languages to a sequence of manual and non-manual gestures plays a crucial role in building a more inclusive society for deaf and hard-of-hearing people. Facial expressions (non-manual), in particular, are respo
Externí odkaz:
http://arxiv.org/abs/2408.15159
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
Autor:
Kerim, Abdulrahman, Chamone, Felipe, Ramos, Washington, Marcolino, Leandro Soriano, Nascimento, Erickson R., Jiang, Richard
Recent semantic segmentation models perform well under standard weather conditions and sufficient illumination but struggle with adverse weather conditions and nighttime. Collecting and annotating training data under these conditions is expensive, ti
Externí odkaz:
http://arxiv.org/abs/2210.05626
Autor:
Kerim, Abdulrahman, Ramos, Washington L. S., Marcolino, Leandro Soriano, Nascimento, Erickson R., Jiang, Richard
Video stabilization plays a central role to improve videos quality. However, despite the substantial progress made by these methods, they were, mainly, tested under standard weather and lighting conditions, and may perform poorly under adverse condit
Externí odkaz:
http://arxiv.org/abs/2208.12763
Autor:
Azevedo, Rafael V., Coutinho, Thiago M., Ferreira, João P., Gomes, Thiago L., Nascimento, Erickson R.
Publikováno v:
In Computers & Graphics November 2024 124
