Visualization techniques provide tools that help scientists identify observed phenomena in scientific simulation. To be useful, these tools must allow the user to extract regions, classify and visualize them, abstract them for simplified representations, and track their evolution. Object Segmentation provides a technique to extract and quantify regions of interest within these massive datasets. This article explores basic algorithms to extract coherent amorphous regions from two-dimensional and three-dimensional scalar unstructured grids. The techniques are applied to datasets from Computational Fluid Dynamics and those from Finite Element Analysis
Abstract — The ability to extract and follow time-varying features in volume data obtained from larg...
Algorithms are described for the generation and adaptation of unstructured grids in two and three di...
This work proposes a new segmentation algorithm for three-dimensional dense point clouds and has bee...
Datasets generated by computer simulations and experiments in Computational Fluid Dynamics tend to b...
Identifying and isolating features is an important part of visualization and a crucial step for the ...
This paper describes techniques, based on the extraction of geometric features, for facilitating the...
Time varying simulations are common in many scientific domains to study the evolution of phenomena o...
Visualizations are well suited to communicate large amounts of complex data. With increasing resolut...
Computer vision and machine learning tools offer an exciting new way for automatically analyzing and...
Scientists studying the dynamics of objects can use these visualization techniques to extract object...
In the past, feature extraction and identification were interesting concepts, but not required to un...
In recent years, there has been a rapid growth in the ability to obtain detailed data on large compl...
Computational Fluid Dynamics (CFD) simulations are routinely performed as part of the design process...
In this thesis we explore machine and deep learning approaches that address keychallenges in high di...
Large-scale simulations are increasingly being used to study complex scientific and engineering phen...
Abstract — The ability to extract and follow time-varying features in volume data obtained from larg...
Algorithms are described for the generation and adaptation of unstructured grids in two and three di...
This work proposes a new segmentation algorithm for three-dimensional dense point clouds and has bee...
Datasets generated by computer simulations and experiments in Computational Fluid Dynamics tend to b...
Identifying and isolating features is an important part of visualization and a crucial step for the ...
This paper describes techniques, based on the extraction of geometric features, for facilitating the...
Time varying simulations are common in many scientific domains to study the evolution of phenomena o...
Visualizations are well suited to communicate large amounts of complex data. With increasing resolut...
Computer vision and machine learning tools offer an exciting new way for automatically analyzing and...
Scientists studying the dynamics of objects can use these visualization techniques to extract object...
In the past, feature extraction and identification were interesting concepts, but not required to un...
In recent years, there has been a rapid growth in the ability to obtain detailed data on large compl...
Computational Fluid Dynamics (CFD) simulations are routinely performed as part of the design process...
In this thesis we explore machine and deep learning approaches that address keychallenges in high di...
Large-scale simulations are increasingly being used to study complex scientific and engineering phen...
Abstract — The ability to extract and follow time-varying features in volume data obtained from larg...
Algorithms are described for the generation and adaptation of unstructured grids in two and three di...
This work proposes a new segmentation algorithm for three-dimensional dense point clouds and has bee...