Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Zanlorensi, Luiz A."'
Autor:
Ribas, Marcelo Eduardo Marques, Mendes, Heloisa Benedet, de Oliveira, Luiz Eduardo Soares, Zanlorensi, Luiz Antonio, de Almeida, Paulo Ricardo Lisboa
In smart cities, it is common practice to define a maximum length of stay for a given parking space to increase the space's rotativity and discourage the usage of individual transportation solutions. However, automatically determining individual car
Externí odkaz:
http://arxiv.org/abs/2411.00158
License Plate Recognition (LPR) plays a critical role in various applications, such as toll collection, parking management, and traffic law enforcement. Although LPR has witnessed significant advancements through the development of deep learning, the
Externí odkaz:
http://arxiv.org/abs/2309.04331
Accurate extraction of the Region of Interest is critical for successful ocular region-based biometrics. In this direction, we propose a new context-based segmentation approach, entitled Ocular Region Context Network (ORCNet), introducing a specific
Externí odkaz:
http://arxiv.org/abs/2204.07456
Autor:
Zanlorensi, Luiz A., Laroca, Rayson, Lucio, Diego R., Santos, Lucas R., Britto Jr., Alceu S., Menotti, David
Publikováno v:
Scientific Reports, vol. 12, p. 17989, 2022
Recently, ocular biometrics in unconstrained environments using images obtained at visible wavelength have gained the researchers' attention, especially with images captured by mobile devices. Periocular recognition has been demonstrated to be an alt
Externí odkaz:
http://arxiv.org/abs/2011.12427
Autor:
Laroca, Rayson, Araujo, Alessandra B., Zanlorensi, Luiz A., de Almeida, Eduardo C., Menotti, David
Publikováno v:
IEEE Access, vol. 9, pp. 67569-67584, 2021
Existing approaches for image-based Automatic Meter Reading (AMR) have been evaluated on images captured in well-controlled scenarios. However, real-world meter reading presents unconstrained scenarios that are way more challenging due to dirt, vario
Externí odkaz:
http://arxiv.org/abs/2009.10181
Ocular biometric systems working in unconstrained environments usually face the problem of small within-class compactness caused by the multiple factors that jointly degrade the quality of the obtained data. In this work, we propose an attribute norm
Externí odkaz:
http://arxiv.org/abs/2002.03985
Autor:
Zanlorensi, Luiz A., Laroca, Rayson, Luz, Eduardo, Britto Jr., Alceu S., Oliveira, Luiz S., Menotti, David
The use of the iris and periocular region as biometric traits has been extensively investigated, mainly due to the singularity of the iris features and the use of the periocular region when the image resolution is not sufficient to extract iris infor
Externí odkaz:
http://arxiv.org/abs/1911.09646
One of the major challenges in ocular biometrics is the cross-spectral scenario, i.e., how to match images acquired in different wavelengths (typically visible (VIS) against near-infrared (NIR)). This article designs and extensively evaluates cross-s
Externí odkaz:
http://arxiv.org/abs/1911.09509
Autor:
Laroca, Rayson, Zanlorensi, Luiz A., Gonçalves, Gabriel R., Todt, Eduardo, Schwartz, William Robson, Menotti, David
Publikováno v:
IET Intelligent Transport Systems, vol. 15, no. 4, pp. 483-503, 2021
This paper presents an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the state-of-the-art YOLO object detector that contains a unified approach for license plate (LP) detection and layout classification t
Externí odkaz:
http://arxiv.org/abs/1909.01754
In this work, we propose to detect the iris and periocular regions simultaneously using coarse annotations and two well-known object detectors: YOLOv2 and Faster R-CNN. We believe coarse annotations can be used in recognition systems based on the iri
Externí odkaz:
http://arxiv.org/abs/1908.00069