The Experts below are selected from a list of 10380 Experts worldwide ranked by ideXlab platform
Robert Jacob - One of the best experts on this subject based on the ideXlab platform.
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Mining Graphs for Understanding Time-Varying Volumetric Data
IEEE Transactions on Visualization and Computer Graphics, 2016Co-Authors: Yi Gu, Chaoli Wang, Tom Peterka, Robert JacobAbstract:A notable recent trend in time-varying Volumetric Data analysis and visualization is to extract Data relationships and represent them in a low-dimensional abstract graph view for visual understanding and making connections to the underlying Data. Nevertheless, the ever-growing size and complexity of Data demands novel techniques that go beyond standard brushing and linking to allow significant reduction of cognition overhead and interaction cost. In this paper, we present a mining approach that automatically extracts meaningful features from a graph-based representation for exploring time-varying Volumetric Data. This is achieved through the utilization of a series of graph analysis techniques including graph simplification, community detection, and visual recommendation. We investigate the most important transition relationships for time-varying Data and evaluate our solution with several time-varying Data sets of different sizes and characteristics. For gaining insights from the Data, we show that our solution is more efficient and effective than simply asking users to extract relationships via standard interaction techniques, especially when the Data set is large and the relationships are complex. We also collect expert feedback to confirm the usefulness of our approach.
Karen O Egiazarian - One of the best experts on this subject based on the ideXlab platform.
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nonlocal transform domain filter for Volumetric Data denoising and reconstruction
IEEE Transactions on Image Processing, 2013Co-Authors: Matteo Maggioni, Vladimir Katkovnik, Karen O EgiazarianAbstract:We present an extension of the BM3D filter to Volumetric Data. The proposed algorithm, BM4D, implements the grouping and collaborative filtering paradigm, where mutually similar d -dimensional patches are stacked together in a (d+1) -dimensional array and jointly filtered in transform domain. While in BM3D the basic Data patches are blocks of pixels, in BM4D we utilize cubes of voxels, which are stacked into a 4-D “group.” The 4-D transform applied on the group simultaneously exploits the local correlation present among voxels in each cube and the nonlocal correlation between the corresponding voxels of different cubes. Thus, the spectrum of the group is highly sparse, leading to very effective separation of signal and noise through coefficient shrinkage. After inverse transformation, we obtain estimates of each grouped cube, which are then adaptively aggregated at their original locations. We evaluate the algorithm on denoising of Volumetric Data corrupted by Gaussian and Rician noise, as well as on reconstruction of Volumetric phantom Data with non-zero phase from noisy and incomplete Fourier-domain (k-space) measurements. Experimental results demonstrate the state-of-the-art denoising performance of BM4D, and its effectiveness when exploited as a regularizer in Volumetric Data reconstruction.
Yi Gu - One of the best experts on this subject based on the ideXlab platform.
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Mining Graphs for Understanding Time-Varying Volumetric Data
IEEE Transactions on Visualization and Computer Graphics, 2016Co-Authors: Yi Gu, Chaoli Wang, Tom Peterka, Robert JacobAbstract:A notable recent trend in time-varying Volumetric Data analysis and visualization is to extract Data relationships and represent them in a low-dimensional abstract graph view for visual understanding and making connections to the underlying Data. Nevertheless, the ever-growing size and complexity of Data demands novel techniques that go beyond standard brushing and linking to allow significant reduction of cognition overhead and interaction cost. In this paper, we present a mining approach that automatically extracts meaningful features from a graph-based representation for exploring time-varying Volumetric Data. This is achieved through the utilization of a series of graph analysis techniques including graph simplification, community detection, and visual recommendation. We investigate the most important transition relationships for time-varying Data and evaluate our solution with several time-varying Data sets of different sizes and characteristics. For gaining insights from the Data, we show that our solution is more efficient and effective than simply asking users to extract relationships via standard interaction techniques, especially when the Data set is large and the relationships are complex. We also collect expert feedback to confirm the usefulness of our approach.
Matteo Maggioni - One of the best experts on this subject based on the ideXlab platform.
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nonlocal transform domain filter for Volumetric Data denoising and reconstruction
IEEE Transactions on Image Processing, 2013Co-Authors: Matteo Maggioni, Vladimir Katkovnik, Karen O EgiazarianAbstract:We present an extension of the BM3D filter to Volumetric Data. The proposed algorithm, BM4D, implements the grouping and collaborative filtering paradigm, where mutually similar d -dimensional patches are stacked together in a (d+1) -dimensional array and jointly filtered in transform domain. While in BM3D the basic Data patches are blocks of pixels, in BM4D we utilize cubes of voxels, which are stacked into a 4-D “group.” The 4-D transform applied on the group simultaneously exploits the local correlation present among voxels in each cube and the nonlocal correlation between the corresponding voxels of different cubes. Thus, the spectrum of the group is highly sparse, leading to very effective separation of signal and noise through coefficient shrinkage. After inverse transformation, we obtain estimates of each grouped cube, which are then adaptively aggregated at their original locations. We evaluate the algorithm on denoising of Volumetric Data corrupted by Gaussian and Rician noise, as well as on reconstruction of Volumetric phantom Data with non-zero phase from noisy and incomplete Fourier-domain (k-space) measurements. Experimental results demonstrate the state-of-the-art denoising performance of BM4D, and its effectiveness when exploited as a regularizer in Volumetric Data reconstruction.
Michelle Noga - One of the best experts on this subject based on the ideXlab platform.
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Real-Time Rendering of Temporal Volumetric Data on a GPU
2011 15th International Conference on Information Visualisation, 2011Co-Authors: Pierre Boulanger, Michelle NogaAbstract:Real-time rendering of static Volumetric Data is generally known to be a memory and computationally intensive process. With the advance of graphic hardware, especially GPU, it is now possible to do this using desktop computers. However, with the evolution of real-time CT and MRI technologies, Volumetric rendering is an even bigger challenge. The first one is how to reduce the Data transmission between the main memory and the graphic memory. The second one is how to efficiently take advantage of the time redundancy which exists in time-varying Volumetric Data. We proposed an optimized compression scheme that explores the time redundancy as well as space redundancy of time-varying Volumetric Data. The compressed Data is then transmitted to graphic memory and directly rendered by the GPU, reducing significantly the Data transfer between main memory and graphic memory.
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MedVis: A Real-Time Immersive Visualization Environment for the Exploration of Medical Volumetric Data
2008 Fifth International Conference BioMedical Visualization: Information Visualization in Medical and Biomedical Informatics, 2008Co-Authors: Rui Shen, Pierre Boulanger, Michelle NogaAbstract:This paper describes the Medical Visualizer, a real-time visualization system for analyzing medical Volumetric Data in various virtual environments, such as autostereoscopic displays, dual-projector screens and immersive environments such as the CAVE. Direct volume rendering is used for visualizing the details of medical Volumetric Data sets without intermediate geometric representations. By interactively manipulating the color and transparency functions through the friendly user interface, radiologists can either inspect the Data set as a whole or focus on a specific region. In our system, 3D texture hardware is employed to accelerate the rendering process. The system is designed to be platform independent, as all virtual reality functions are separated from kernel functions. Due to its modular design, our system can be easily extended to other virtual environments, and new functions can be incorporated rapidly.
