A5004248979- Advanced Numerical Analysis Techniques
- Computer Graphics and Visualization Techniques
- 3D Shape Modeling and Analysis
- Human Pose and Action Recognition
- earthquake and tectonic studies
- Neural Networks and Applications
- Semantic Web and Ontologies
- Medical Image Segmentation Techniques
- Seismology and Earthquake Studies
- Video Analysis and Summarization
- Earthquake Detection and Analysis
- Business Process Modeling and Analysis
- Video Surveillance and Tracking Methods
- Advanced Neural Network Applications
- Data Visualization and Analytics
- Image and Signal Denoising Methods
- Advanced Vision and Imaging
- Model Reduction and Neural Networks
- Image Processing and 3D Reconstruction
- Advanced Image Processing Techniques
- Biometric Identification and Security
- Graph Theory and Algorithms
- Advanced Image Fusion Techniques
- Brain Tumor Detection and Classification
- Advanced Steganography and Watermarking Techniques
Universidade do Vale do Rio dos Sinos
2023-2025
University of Coimbra
2022-2023
Instituto Nacional de Matemática Pura e Aplicada
2023
Pontifical Catholic University of Rio de Janeiro
2017-2021
IBM Research - Brazil
2018
This work investigates the use of smooth neural networks for modeling dynamic variations implicit surfaces under level set equation (LSE). For this, it extends representation to space-time ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> × ℝ, which opens up mechanisms continuous geometric transformations. Examples include evolving an initial surface towards general vector fields, smoothing and sharpening using mean curvature equation,...
We present MR-Net, a general architecture for multiresolution neural networks, and framework imaging applications based on this architecture. Our coordinate-based networks are continuous both in space scale as they composed of multiple stages that progressively add finer details. Besides that, compact efficient representation. show examples image representation to texture magnification, minification, antialiasing.
Summary The objective of this work is to develop and train feedforward artificial neural networks (ANNs) on the forecasting layer permeability in heterogeneous reservoirs. results are validated by comparing model outputs with curves computed from production logging data. Production logs used as targets model. A flow-profile interpretation method compute continuous free wellbore skin effects. In addition, segmentation techniques applied high-resolution ultrasonic image logs. These provide not...
This survey presents methods that use neural networks for implicit representations of 3D geometry — functions. We explore the different aspects functions shape modeling and synthesis. aim to provide a theoretical analysis reconstruction using deep introduce discussion between researchers interested in this research field.
Finding concepts considering their meaning and semantic relations in a document corpus is an important challenging task. In this paper, we present our contributions on how to understand unstructured data one or multiple documents. Generally, the current literature concentrates efforts structuring knowledge by identifying entities data. test hypothesis that hyperknowledge specifications are capable of defining rich among documents extracted facts. The main evidence supporting fact was built...
Pose estimation is a challenging task in computer vision that has many applications, as for example: motion capture, medical analysis, human posture monitoring, and robotics. In other words, it main tool to enable machines do understand patterns videos or images. Performing this real-time while maintaining accuracy precision critical of these applications. Several papers propose real time approaches considering deep neural networks pose estimation. However, most cases they fail when run-time...
Neural fields have emerged as a promising framework for representing different types of signals. This tutorial focus on the existing literature and shares practical insights derived from hands-on experimentation with neural fields, specifically in approximating implicit functions surfaces. Our emphasis lies strategies leveraging differential geometry concepts to enhance training outcomes showcase applications within this domain.
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Earth structural heterogeneities have a remarkable role in the petroleum economy for both exploration and production projects. Automatic detection of detailed is challenging when considering modern machine learning techniques like deep neural networks. Typically, these can be an excellent tool assisted interpretation such heterogeneities, but it heavily depends on amount data to trained. We propose efficient cost-effective architecture detecting seismic using Convolutional Neural Networks...
Earth structural heterogeneities have a remarkable role in the petroleum economy for both exploration and production projects. Automatic detection of detailed is challenging when considering modern machine learning techniques like deep neural networks. Typically, these can be an excellent tool assisted interpretation such heterogeneities, but it heavily depends on amount data to trained. We propose efficient cost-effective architecture detecting seismic using Convolutional Neural Networks...