Zobrazeno 1 - 10
of 137
pro vyhledávání: '"Hugo Proença"'
Publikováno v:
Applied Sciences, Vol 14, Iss 12, p 5298 (2024)
Detecting anomalies in texture has become a significant concern across various industrial processes. One prevalent application of this is in inspecting patterned textures, especially in the domain of fabric defect detection, which is a commonly encou
Externí odkaz:
https://doaj.org/article/2017a90955fd42b89c0e88015f94a69f
Publikováno v:
Sensors, Vol 23, Iss 5, p 2456 (2023)
Periocular recognition has emerged as a particularly valuable biometric identification method in challenging scenarios, such as partially occluded faces due to COVID-19 protective masks masks, in which face recognition might not be applicable. This w
Externí odkaz:
https://doaj.org/article/794f773eacbc4cd7804ed21c43285dcf
Publikováno v:
IET Biometrics, Vol 11, Iss 4, Pp 277-278 (2022)
Externí odkaz:
https://doaj.org/article/d7ef0716694242a7826988f623066ec3
Autor:
Eduardo Assunção, Pedro D. Gaspar, Khadijeh Alibabaei, Maria P. Simões, Hugo Proença, Vasco N. G. J. Soares, João M. L. P. Caldeira
Publikováno v:
Future Internet, Vol 14, Iss 11, p 323 (2022)
Within the scope of precision agriculture, many applications have been developed to support decision making and yield enhancement. Fruit detection has attracted considerable attention from researchers, and it can be used offline. In contrast, some ap
Externí odkaz:
https://doaj.org/article/61a8dacc8f89409b9e62227ba2da96bd
Autor:
Eduardo Assunção, Pedro D. Gaspar, Ricardo Mesquita, Maria P. Simões, Khadijeh Alibabaei, André Veiros, Hugo Proença
Publikováno v:
Remote Sensing, Vol 14, Iss 17, p 4217 (2022)
Portable devices play an essential role where edge computing is necessary and mobility is required (e.g., robots in agriculture within remote-sensing applications). With the increasing applications of deep neural networks (DNNs) and accelerators for
Externí odkaz:
https://doaj.org/article/3d2eff257dbf4c449ae09ebc4baeeb8e
Publikováno v:
Applied Sciences, Vol 12, Iss 15, p 7385 (2022)
Brain tissue segmentation is an important component of the clinical diagnosis of brain diseases using multi-modal magnetic resonance imaging (MR). Brain tissue segmentation has been developed by many unsupervised methods in the literature. The most c
Externí odkaz:
https://doaj.org/article/036eb46b379941858d7b22299271eb88
Autor:
Bruno Degardin, Hugo Proença
Publikováno v:
Applied Sciences, Vol 11, Iss 18, p 8324 (2021)
The visual recognition and understanding of human actions remain an active research domain of computer vision, being the scope of various research works over the last two decades. The problem is challenging due to its many interpersonal variations in
Externí odkaz:
https://doaj.org/article/a3deec5ff5f94d3289c2117e202781dd
Publikováno v:
Applied Sciences, Vol 10, Iss 16, p 5608 (2020)
Human Attribute Recognition (HAR) is a highly active research field in computer vision and pattern recognition domains with various applications such as surveillance or fashion. Several approaches have been proposed to tackle the particular challenge
Externí odkaz:
https://doaj.org/article/7934387249724a318373132f04ffa3a0
Publikováno v:
IET Computer Vision, Vol 9, Iss 2, Pp 216-225 (2015)
Sparse representations for classification (SRC) are considered a relevant advance to the biometrics field, but are particularly sensitive to data misalignments. In previous studies, such misalignments were compensated for by finding appropriate geome
Externí odkaz:
https://doaj.org/article/4c74f7cbfbd847058558925c431c4c67
Autor:
Tiago Roxo, Hugo Proença
Publikováno v:
IEEE Transactions on Biometrics, Behavior, and Identity Science. 3:573-582
Soft biometrics analysis is seen as an important research topic, given its relevance to various applications. However, even though it is frequently seen as a solved task, it can still be very hard to perform in wild conditions, under varying image co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42bc3137b2378b8f0fdf9c3ea925c79f
https://doi.org/10.1109/tbiom.2021.3100926
https://doi.org/10.1109/tbiom.2021.3100926