Computer vision researcher with experience in machine learning, pattern recognition, biometric recognition, 3D human pose estimation and domain adaptation. B.Sc. and M.Sc. in Computer Science obtained at the State University of São Paulo (UNESP). (Source: Lattes Curriculum)
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Human pose estimation in images is an important and challenging problem in Computer Vision. Currently, methods that employ deep learning techniques excel in the task of 2D human pose estimation. 2D poses can be used in a diverse and broad set of applications, of great relevance to society. However, the use of 3D poses can bring even more accurate and robust results. The objective of this …
In the last decades, for reasons of safety or convenience, biometric characteristics are increasingly being used to identify individuals who wish to have access to systems or places, and facial features are one of the most used characteristics for this purpose. For biometric identification to be effective, the rates of recognition accuracy must be high. However, these rates can be very lo…
Monocular human pose estimation is an important Computer Vision problem, aimed to estimate the human body shape based on a single RGB image. Currently, methods that employ deep learning techniques excel in the task of 2D human pose estimation, which can be used in a diverse set of applications. However, the use of 3D poses can bring more accurate and robust results. As 3D pose annotations…
| 2 / 2 | Completed scholarships in Brazil |
| 1 / 1 | Completed scholarships abroad |
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