The Experts below are selected from a list of 117 Experts worldwide ranked by ideXlab platform

Satoshi Tanaka - One of the best experts on this subject based on the ideXlab platform.

  • grid independent metropolis sampling for volume visualization
    International Journal of Modeling Simulation and Scientific Computing, 2010
    Co-Authors: Satoshi Tanaka, Hideo Nakajima, Kyoko Hasegawa, Susumu Nakata, Takuya Hatta, Frederika Rambu Ngana, Takuma Kawamura, Naohisa Sakamoto, Koji Koyamada
    Abstract:

    We propose a method of sampling regular and irregular-grid volume data for visualization. The method is based on the Metropolis algorithm that is a type of Monte Carlo technique. Our method enables "importance sampling" of local regions of interest in the visualization by generating Sample points intensively in regions where a user-specified transfer function takes the peak values. The Generated Sample-point distribution is independent of the grid structure of the given volume data. Therefore, our method is applicable to irregular grids as well as regular grids. We demonstrate the effectiveness of our method by applying it to regular cubic grids and irregular tetrahedral grids with adaptive cell sizes. We visualize volume data by projecting the Generated Sample points onto the 2D image plane. We tested our sampling with three rendering models: an X-ray model, a simple illuminant particle model, and an illuminant particle model with light-attenuation effects. The grid-independency and the efficiency in the parallel processing mean that our method is suitable for visualizing large-scale volume data. The former means that the required number of Sample points is proportional to the number of 2D pixels, not the number of 3D voxels. The latter means that our method can be easily accelerated on the multiple-CPU and/or GPU platforms. We also show that our method can work with adaptive space partitioning of volume data, which also enables us to treat large-scale/complex volume data easily.

  • IIH-MSP - Volume Visualization with Grid-Independent Adaptive Monte Carlo Sampling
    2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009
    Co-Authors: Hideo Nakajima, Kyoko Hasegawa, Susumu Nakata, Satoshi Tanaka
    Abstract:

    We propose a method of sampling regular and irregular-grid volume data for visualization. The method is based on the Metropolis algorithm that is a type of Monte Carlo technique. Our method enables ‘importance sampling’ of local regions of interest in the visualization by generating Sample points intensively in regions where a user-specified transfer function takes the peak values. The Generated Sample-point distribution is independent of the grid structure of the given volume data. Therefore, our method is applicable to irregular grids as well as regular grids. In this paper, we also demonstrate features of adaptive sampling in our method. We visualize volume data by projecting the Generated Sample points onto the 2D image plane. In this research, we propose to improve efficiency of the Monte Carlo volume graphics by using space partition of volumic MPU.

  • Volume Visualization with Grid-Independent Adaptive Monte Carlo Sampling
    2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009
    Co-Authors: Hideo Nakajima, Kyoko Hasegawa, Susumu Nakata, Satoshi Tanaka
    Abstract:

    We propose a method of sampling regular and irregular-grid volume data for visualization. The method is based on the Metropolis algorithm that is a type of Monte Carlo technique. Our method enables `importance sampling' of local regions of interest in the visualization by generating Sample points intensively in regions where a user-specified transfer function takes the peak values. The Generated Sample-point distribution is independent of the grid structure of the given volume data. Therefore, our method is applicable to irregular grids as well as regular grids. In this paper, we also demonstrate features of adaptive sampling in our method. We visualize volume data by projecting the Generated Sample points onto the 2D image plane. In this research, we propose to improve efficiency of the Monte Carlo volume graphics by using space partition of volumic MPU.

Hideo Nakajima - One of the best experts on this subject based on the ideXlab platform.

  • grid independent metropolis sampling for volume visualization
    International Journal of Modeling Simulation and Scientific Computing, 2010
    Co-Authors: Satoshi Tanaka, Hideo Nakajima, Kyoko Hasegawa, Susumu Nakata, Takuya Hatta, Frederika Rambu Ngana, Takuma Kawamura, Naohisa Sakamoto, Koji Koyamada
    Abstract:

    We propose a method of sampling regular and irregular-grid volume data for visualization. The method is based on the Metropolis algorithm that is a type of Monte Carlo technique. Our method enables "importance sampling" of local regions of interest in the visualization by generating Sample points intensively in regions where a user-specified transfer function takes the peak values. The Generated Sample-point distribution is independent of the grid structure of the given volume data. Therefore, our method is applicable to irregular grids as well as regular grids. We demonstrate the effectiveness of our method by applying it to regular cubic grids and irregular tetrahedral grids with adaptive cell sizes. We visualize volume data by projecting the Generated Sample points onto the 2D image plane. We tested our sampling with three rendering models: an X-ray model, a simple illuminant particle model, and an illuminant particle model with light-attenuation effects. The grid-independency and the efficiency in the parallel processing mean that our method is suitable for visualizing large-scale volume data. The former means that the required number of Sample points is proportional to the number of 2D pixels, not the number of 3D voxels. The latter means that our method can be easily accelerated on the multiple-CPU and/or GPU platforms. We also show that our method can work with adaptive space partitioning of volume data, which also enables us to treat large-scale/complex volume data easily.

  • IIH-MSP - Volume Visualization with Grid-Independent Adaptive Monte Carlo Sampling
    2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009
    Co-Authors: Hideo Nakajima, Kyoko Hasegawa, Susumu Nakata, Satoshi Tanaka
    Abstract:

    We propose a method of sampling regular and irregular-grid volume data for visualization. The method is based on the Metropolis algorithm that is a type of Monte Carlo technique. Our method enables ‘importance sampling’ of local regions of interest in the visualization by generating Sample points intensively in regions where a user-specified transfer function takes the peak values. The Generated Sample-point distribution is independent of the grid structure of the given volume data. Therefore, our method is applicable to irregular grids as well as regular grids. In this paper, we also demonstrate features of adaptive sampling in our method. We visualize volume data by projecting the Generated Sample points onto the 2D image plane. In this research, we propose to improve efficiency of the Monte Carlo volume graphics by using space partition of volumic MPU.

  • Volume Visualization with Grid-Independent Adaptive Monte Carlo Sampling
    2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009
    Co-Authors: Hideo Nakajima, Kyoko Hasegawa, Susumu Nakata, Satoshi Tanaka
    Abstract:

    We propose a method of sampling regular and irregular-grid volume data for visualization. The method is based on the Metropolis algorithm that is a type of Monte Carlo technique. Our method enables `importance sampling' of local regions of interest in the visualization by generating Sample points intensively in regions where a user-specified transfer function takes the peak values. The Generated Sample-point distribution is independent of the grid structure of the given volume data. Therefore, our method is applicable to irregular grids as well as regular grids. In this paper, we also demonstrate features of adaptive sampling in our method. We visualize volume data by projecting the Generated Sample points onto the 2D image plane. In this research, we propose to improve efficiency of the Monte Carlo volume graphics by using space partition of volumic MPU.

Susumu Nakata - One of the best experts on this subject based on the ideXlab platform.

  • grid independent metropolis sampling for volume visualization
    International Journal of Modeling Simulation and Scientific Computing, 2010
    Co-Authors: Satoshi Tanaka, Hideo Nakajima, Kyoko Hasegawa, Susumu Nakata, Takuya Hatta, Frederika Rambu Ngana, Takuma Kawamura, Naohisa Sakamoto, Koji Koyamada
    Abstract:

    We propose a method of sampling regular and irregular-grid volume data for visualization. The method is based on the Metropolis algorithm that is a type of Monte Carlo technique. Our method enables "importance sampling" of local regions of interest in the visualization by generating Sample points intensively in regions where a user-specified transfer function takes the peak values. The Generated Sample-point distribution is independent of the grid structure of the given volume data. Therefore, our method is applicable to irregular grids as well as regular grids. We demonstrate the effectiveness of our method by applying it to regular cubic grids and irregular tetrahedral grids with adaptive cell sizes. We visualize volume data by projecting the Generated Sample points onto the 2D image plane. We tested our sampling with three rendering models: an X-ray model, a simple illuminant particle model, and an illuminant particle model with light-attenuation effects. The grid-independency and the efficiency in the parallel processing mean that our method is suitable for visualizing large-scale volume data. The former means that the required number of Sample points is proportional to the number of 2D pixels, not the number of 3D voxels. The latter means that our method can be easily accelerated on the multiple-CPU and/or GPU platforms. We also show that our method can work with adaptive space partitioning of volume data, which also enables us to treat large-scale/complex volume data easily.

  • IIH-MSP - Volume Visualization with Grid-Independent Adaptive Monte Carlo Sampling
    2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009
    Co-Authors: Hideo Nakajima, Kyoko Hasegawa, Susumu Nakata, Satoshi Tanaka
    Abstract:

    We propose a method of sampling regular and irregular-grid volume data for visualization. The method is based on the Metropolis algorithm that is a type of Monte Carlo technique. Our method enables ‘importance sampling’ of local regions of interest in the visualization by generating Sample points intensively in regions where a user-specified transfer function takes the peak values. The Generated Sample-point distribution is independent of the grid structure of the given volume data. Therefore, our method is applicable to irregular grids as well as regular grids. In this paper, we also demonstrate features of adaptive sampling in our method. We visualize volume data by projecting the Generated Sample points onto the 2D image plane. In this research, we propose to improve efficiency of the Monte Carlo volume graphics by using space partition of volumic MPU.

  • Volume Visualization with Grid-Independent Adaptive Monte Carlo Sampling
    2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009
    Co-Authors: Hideo Nakajima, Kyoko Hasegawa, Susumu Nakata, Satoshi Tanaka
    Abstract:

    We propose a method of sampling regular and irregular-grid volume data for visualization. The method is based on the Metropolis algorithm that is a type of Monte Carlo technique. Our method enables `importance sampling' of local regions of interest in the visualization by generating Sample points intensively in regions where a user-specified transfer function takes the peak values. The Generated Sample-point distribution is independent of the grid structure of the given volume data. Therefore, our method is applicable to irregular grids as well as regular grids. In this paper, we also demonstrate features of adaptive sampling in our method. We visualize volume data by projecting the Generated Sample points onto the 2D image plane. In this research, we propose to improve efficiency of the Monte Carlo volume graphics by using space partition of volumic MPU.

Kyoko Hasegawa - One of the best experts on this subject based on the ideXlab platform.

  • grid independent metropolis sampling for volume visualization
    International Journal of Modeling Simulation and Scientific Computing, 2010
    Co-Authors: Satoshi Tanaka, Hideo Nakajima, Kyoko Hasegawa, Susumu Nakata, Takuya Hatta, Frederika Rambu Ngana, Takuma Kawamura, Naohisa Sakamoto, Koji Koyamada
    Abstract:

    We propose a method of sampling regular and irregular-grid volume data for visualization. The method is based on the Metropolis algorithm that is a type of Monte Carlo technique. Our method enables "importance sampling" of local regions of interest in the visualization by generating Sample points intensively in regions where a user-specified transfer function takes the peak values. The Generated Sample-point distribution is independent of the grid structure of the given volume data. Therefore, our method is applicable to irregular grids as well as regular grids. We demonstrate the effectiveness of our method by applying it to regular cubic grids and irregular tetrahedral grids with adaptive cell sizes. We visualize volume data by projecting the Generated Sample points onto the 2D image plane. We tested our sampling with three rendering models: an X-ray model, a simple illuminant particle model, and an illuminant particle model with light-attenuation effects. The grid-independency and the efficiency in the parallel processing mean that our method is suitable for visualizing large-scale volume data. The former means that the required number of Sample points is proportional to the number of 2D pixels, not the number of 3D voxels. The latter means that our method can be easily accelerated on the multiple-CPU and/or GPU platforms. We also show that our method can work with adaptive space partitioning of volume data, which also enables us to treat large-scale/complex volume data easily.

  • IIH-MSP - Volume Visualization with Grid-Independent Adaptive Monte Carlo Sampling
    2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009
    Co-Authors: Hideo Nakajima, Kyoko Hasegawa, Susumu Nakata, Satoshi Tanaka
    Abstract:

    We propose a method of sampling regular and irregular-grid volume data for visualization. The method is based on the Metropolis algorithm that is a type of Monte Carlo technique. Our method enables ‘importance sampling’ of local regions of interest in the visualization by generating Sample points intensively in regions where a user-specified transfer function takes the peak values. The Generated Sample-point distribution is independent of the grid structure of the given volume data. Therefore, our method is applicable to irregular grids as well as regular grids. In this paper, we also demonstrate features of adaptive sampling in our method. We visualize volume data by projecting the Generated Sample points onto the 2D image plane. In this research, we propose to improve efficiency of the Monte Carlo volume graphics by using space partition of volumic MPU.

  • Volume Visualization with Grid-Independent Adaptive Monte Carlo Sampling
    2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009
    Co-Authors: Hideo Nakajima, Kyoko Hasegawa, Susumu Nakata, Satoshi Tanaka
    Abstract:

    We propose a method of sampling regular and irregular-grid volume data for visualization. The method is based on the Metropolis algorithm that is a type of Monte Carlo technique. Our method enables `importance sampling' of local regions of interest in the visualization by generating Sample points intensively in regions where a user-specified transfer function takes the peak values. The Generated Sample-point distribution is independent of the grid structure of the given volume data. Therefore, our method is applicable to irregular grids as well as regular grids. In this paper, we also demonstrate features of adaptive sampling in our method. We visualize volume data by projecting the Generated Sample points onto the 2D image plane. In this research, we propose to improve efficiency of the Monte Carlo volume graphics by using space partition of volumic MPU.

Christian Riess - One of the best experts on this subject based on the ideXlab platform.

  • GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data
    IEEE Geoscience and Remote Sensing Letters, 2018
    Co-Authors: Amirabbas Davari, Erchan Aptoula, Berrin Yanikoglu, Andreas Maier, Christian Riess
    Abstract:

    The amount of training data that is required to train a classifier scales with the dimensionality of the feature data. In hyperspectral remote sensing (HSRS), feature data can potentially become very high dimensional. However, the amount of training data is oftentimes limited. Thus, one of the core challenges in HSRS is how to perform multiclass classification using only relatively few training data points. In this letter, we address this issue by enriching the feature matrix with synthetically Generated Sample points. These synthetic data are Sampled from a Gaussian mixture model (GMM) fitted to each class of the limited training data. Although the true distribution of features may not be perfectly modeled by the fitted GMM, we demonstrate that a moderate augmentation by these synthetic Samples can effectively replace a part of the missing training Samples. Doing so, the median gain in classification performance is 5% on two datasets. This performance gain is stable for variations in the number of added Samples, which makes it easy to apply this method to real-world applications.