Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Zobeir Raisi"'
Autor:
Zobeir Raisi, John Zelek
Publikováno v:
Frontiers in Robotics and AI, Vol 11 (2024)
We live in a visual world where text cues are abundant in urban environments. The premise for our work is for robots to capitalize on these text features for visual place recognition. A new technique is introduced that uses an end-to-end scene text d
Externí odkaz:
https://doaj.org/article/f5371a566fa54a39892be053bbc9f723
Autor:
Zobeir Raisi, John Zelek
Publikováno v:
International Journal of Industrial Electronics, Control and Optimization, Vol 6, Iss 3, Pp 171-182 (2023)
Scene text detection frameworks heavily rely on optimization methods for their successful operation. Choosing an appropriate optimizer is essential to performing recent scene text detection models. However, recent deep learning methods often employ v
Externí odkaz:
https://doaj.org/article/ff169f1728384d82897efee789dca30a
Publikováno v:
International Journal of Information and Communication Technology Research, Vol 4, Iss 1, Pp 57-64 (2012)
This paper proposed a scenario for using a CBIR (Content-Based Image Retrieval) system in tourism application. Several CBIR algorithms are studied and applied for the proposed scenario. An image database specialized for this application is made to be
Externí odkaz:
https://doaj.org/article/b01b48f28c04458fb277eb0209a3a1a6
Publikováno v:
Machine Learning Algorithms for Signal and Image Processing. :161-200
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eb21de97e268ba513720dff73c1567fc
https://doi.org/10.1002/9781119861850.ch10
https://doi.org/10.1002/9781119861850.ch10
Publikováno v:
2022 26th International Conference on Pattern Recognition (ICPR).
Recent text detection frameworks require several handcrafted components such as anchor generation, non-maximum suppression (NMS), or multiple processing stages (e.g. label generation) to detect arbitrarily shaped text images. In contrast, we propose
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::535e85dc00d0ae3efc1b5356e153d824
https://doi.org/10.1109/icpr56361.2022.9956488
https://doi.org/10.1109/icpr56361.2022.9956488
Publikováno v:
Journal of Computational Vision and Imaging Systems. 6:1-4
Recent state-of-the-art scene text recognition methods are primarily based on Recurrent Neural Networks (RNNs), however, these methods require one-dimensional (1D) features and are not designed for recognizing irregular-text instances due to the loss
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::924fb1e8fc0e9a8c8d532f3d58dba26e
https://doi.org/10.15353/jcvis.v6i1.3533
https://doi.org/10.15353/jcvis.v6i1.3533
Publikováno v:
Journal of Computational Vision and Imaging Systems. 6:1-5
The reported accuracy of recent state-of-the-art text detection methods, mostly deep learning approaches, is in the order of 80% to 90% on standard benchmark datasets. These methods have relaxed some of the restrictions of structured text and environ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c48de97738bbd4c6209daa7a3482a27a
https://doi.org/10.15353/jcvis.v6i1.3543
https://doi.org/10.15353/jcvis.v6i1.3543
Autor:
Zobeir Raisi, John Zelek
Publikováno v:
2022 19th Conference on Robots and Vision (CRV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7ee62553fd042ecc2fd5399b3a06d7b1
https://doi.org/10.1109/crv55824.2022.00026
https://doi.org/10.1109/crv55824.2022.00026
Publikováno v:
CVPR Workshops
A major limitation to most state-of-the-art visual localization methods is their ineptitude to make use of ubiquitous signs and directions that are typically intuitive to humans. Localization methods can greatly benefit from a system capable of reaso
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1f09e6dcb98baf2960fc6ee359c4d6ff
https://doi.org/10.1109/cvprw53098.2021.00353
https://doi.org/10.1109/cvprw53098.2021.00353
Publikováno v:
CRV
Positional Encoding (PE) plays a vital role in a Transformer’s ability to capture the order of sequential information, allowing it to overcome the permutation equivarience property. Recent state-of-the-art Transformer-based scene text recognition m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8097ed2ec90a0fc02df21370e9b610fb
https://doi.org/10.1109/crv52889.2021.00024
https://doi.org/10.1109/crv52889.2021.00024