The Experts below are selected from a list of 279 Experts worldwide ranked by ideXlab platform

Mohamed Tajine - One of the best experts on this subject based on the ideXlab platform.

  • Moment-Based Angular Difference Estimation Between Two Tomographic Projections in 2D and 3D
    Journal of Mathematical Imaging and Vision, 2017
    Co-Authors: Minh-son Phan, Etienne Baudrier, Loïc Mazo, Mohamed Tajine
    Abstract:

    This paper introduces a new method for estimating the Angular Difference between two tomographic projections belonging to a set of projections taken at unknown directions in 2D and 3D. Our method relies on the projection neighbor selection in projection moment space, the calculation of the Angular Differences between these neighboring projections using moment properties and a projection moment neighborhood graph. The accuracy and the robustness of our method are shown on a test database including fifty 2D and 3D gray-level images at different resolutions and with different levels of noise.

  • Estimation of Angular Difference between tomographic projections taken at unknown directions in 3D
    2015
    Co-Authors: Minh Phan, Etienne Baudrier, Loïc Mazo, Mohamed Tajine
    Abstract:

    This paper deals with the estimation of Angular Difference between two tomographic projections belonging to a set of projections taken at unknown directions. The proposed method extends our former work from 2D to 3D. The method is potential for many applications such as projection refinement or projection classification, which are important in the process of tomographic reconstruction. Unlike to common line based Angular estimation, the proposed method does not need reference projections. Our method relies on the selection of projection neighbors with local adaptive thresholds, the calculus of the Angular Difference for neighboring projections by using properties of moments. The accuracy and the robustness of our method are shown on a test database including 50 3D gray-level images at different resolutions and with different levels of noise.

  • ICIP - Estimation of Angular Difference between tomographic projections taken at unknown directions in 3D
    2015 IEEE International Conference on Image Processing (ICIP), 2015
    Co-Authors: Minh-son Phan, Etienne Baudrier, Loïc Mazo, Mohamed Tajine
    Abstract:

    This paper deals with the estimation of Angular Difference between two tomographic projections belonging to a set of projections taken at unknown directions. The proposed method extends our former work from 2D to 3D. The method is potential for many applications such as projection refinement or projection classification, which are important in the process of tomographic reconstruction. Unlike to common line based Angular estimation, the proposed method does not need reference projections. Our method relies on the selection of projection neighbors with local adaptive thresholds, the calculus of the Angular Difference for neighboring projections by using properties of moments. The accuracy and the robustness of our method are shown on a test database including 50 3D gray-level images at different resolutions and with different levels of noise.

  • Angular Difference measure between tomographic projections taken at unknown directions in 2D
    2014
    Co-Authors: Minh-son Phan, Etienne Baudrier, Loïc Mazo, Mohamed Tajine
    Abstract:

    This paper introduces a new measure for estimating the Angular Difference between two tomographic projections belonging to a set of projections taken at unknown directions. The measure is potential for many applications such as projection refinement or projection classification, which are important in the process of tomographic reconstruction. Our measure relies on the construction of a neighborhood graph for projection moments, the calculus of the Angular Difference for neighboring projections and the computation of geodesics on this graph. The accuracy and the robustness of our measure is shown on a test database including 50 2D gray-level images at different resolutions and with different levels of noise.

  • ICIP - Angular Difference measure between tomographic projections taken at unknown directions in 2D
    2014 IEEE International Conference on Image Processing (ICIP), 2014
    Co-Authors: Minh-son Phan, Etienne Baudrier, Loïc Mazo, Mohamed Tajine
    Abstract:

    This paper introduces a new measure for estimating the Angular Difference between two tomographic projections belonging to a set of projections taken at unknown directions. The measure is potential for many applications such as projection refinement or projection classification, which are important in the process of tomographic reconstruction. Our measure relies on the construction of a neighborhood graph for projection moments, the calculus of the Angular Difference for neighboring projections and the computation of geodesics on this graph. The accuracy and the robustness of our measure is shown on a test database including 50 2D gray-level images at different resolutions and with different levels of noise.

Danielle Dias - One of the best experts on this subject based on the ideXlab platform.

  • A Multi-Representational Fusion of Time Series for Pixelwise Classification
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
    Co-Authors: Danielle Dias, Allan Pinto, Ulisses Dias, Rubens Lamparelli, Guerric Le Maire, Ricardo Da S Torres
    Abstract:

    This paper addresses the pixelwise classification problem based on temporal profiles, which are encoded in two-dimensional representations based on Recurrence Plots, Gramian Angular/Difference Fields, and Markov Transition Field. We propose a multi-representational fusion scheme that exploits the complementary view provided by those time series representations , and different data-driven feature extractors and classifiers. We validate our ensemble scheme in the problem related to the classification of eucalyptus plantations in remote sensing images. Achieved results demonstrate that our proposal overcomes recently proposed baselines, and now represents the new state-of-the-art classification solution for the target dataset.

  • Image-Based Time Series Representations for Pixelwise Eucalyptus Region Classification: A Comparative Study
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Danielle Dias, Ulisses Dias, Rubens Lamparelli, Nathalia Menini, Guerric Le Maire, Ricardo Da S. Torres
    Abstract:

    Pixelwise image classification based on time series profiles has been very effective in several applications. In this letter, we investigate recently proposed image-based time series encoding approaches [e.g., Gramian Angular summation field/Gramian Angular Difference field (GASF/GADF) and Markov transition field (MTF)] to support the identification of eucalyptus regions in remote sensing images. We perform a comparative study concerning the combination of image-based representations suitable for encoding the most important time series patterns with the ability of state-of-the-art deep-learning-based approaches for characterizing image visual properties. The comparative study demonstrates that the evaluated image representations, combined with different deep learning feature extractors lead to highly effective classification results, which are superior to those of recently proposed methods for time-series-based eucalyptus plantation detection.

Teiichiro Ogawa - One of the best experts on this subject based on the ideXlab platform.

  • translational energy distributions and Angular Difference doppler profiles of the excited hydrogen atom produced in e c2h4 collisions dissociation dynamics of ethylene
    Journal of Chemical Physics, 1992
    Co-Authors: Nobuaki Yonekura, Keiji Nakashima, Teiichiro Ogawa
    Abstract:

    Formation of an excited hydrogen atom (H*) through electron‐impact dissociation of ethylene has been investigated by measuring Doppler profiles of the Balmer‐β line and their Angular dependence at an optical resolution of 0.007 nm. The Doppler profiles show a clear anisotropy. The translational energy distribution (TED) and the Angular Difference Doppler profile were obtained. There are four major dissociation processes for the formation of H*(n=4). Component 1 has a peak of TED at 1 eV, is produced in a perpendicular distribution, and should be produced by predissociation through the Rydberg states converging to the (1b1u)−1 state. Component 2 has a peak of TED at 1.8 eV, is produced in a parallel distribution, and should be produced through the Rydberg states converging to the (2ag)−1 state. Component 3 has a peak of TED at 2–6 eV and is produced in a parallel distribution. Component 4 has a peak of TED at 5–10 eV. Molecular orientation at the time of excitation was estimated; the molecular plane is per...

  • Translational energy distributions and Angular Difference Doppler profiles of the excited hydrogen atom produced in e‐C2H4 collisions: Dissociation dynamics of ethylene
    The Journal of Chemical Physics, 1992
    Co-Authors: Nobuaki Yonekura, Keiji Nakashima, Teiichiro Ogawa
    Abstract:

    Formation of an excited hydrogen atom (H*) through electron‐impact dissociation of ethylene has been investigated by measuring Doppler profiles of the Balmer‐β line and their Angular dependence at an optical resolution of 0.007 nm. The Doppler profiles show a clear anisotropy. The translational energy distribution (TED) and the Angular Difference Doppler profile were obtained. There are four major dissociation processes for the formation of H*(n=4). Component 1 has a peak of TED at 1 eV, is produced in a perpendicular distribution, and should be produced by predissociation through the Rydberg states converging to the (1b1u)−1 state. Component 2 has a peak of TED at 1.8 eV, is produced in a parallel distribution, and should be produced through the Rydberg states converging to the (2ag)−1 state. Component 3 has a peak of TED at 2–6 eV and is produced in a parallel distribution. Component 4 has a peak of TED at 5–10 eV. Molecular orientation at the time of excitation was estimated; the molecular plane is per...

Ricardo Da S. Torres - One of the best experts on this subject based on the ideXlab platform.

  • Image-Based Time Series Representations for Pixelwise Eucalyptus Region Classification: A Comparative Study
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Danielle Dias, Ulisses Dias, Rubens Lamparelli, Nathalia Menini, Guerric Le Maire, Ricardo Da S. Torres
    Abstract:

    Pixelwise image classification based on time series profiles has been very effective in several applications. In this letter, we investigate recently proposed image-based time series encoding approaches [e.g., Gramian Angular summation field/Gramian Angular Difference field (GASF/GADF) and Markov transition field (MTF)] to support the identification of eucalyptus regions in remote sensing images. We perform a comparative study concerning the combination of image-based representations suitable for encoding the most important time series patterns with the ability of state-of-the-art deep-learning-based approaches for characterizing image visual properties. The comparative study demonstrates that the evaluated image representations, combined with different deep learning feature extractors lead to highly effective classification results, which are superior to those of recently proposed methods for time-series-based eucalyptus plantation detection.

Minh-son Phan - One of the best experts on this subject based on the ideXlab platform.

  • Moment-Based Angular Difference Estimation Between Two Tomographic Projections in 2D and 3D
    Journal of Mathematical Imaging and Vision, 2017
    Co-Authors: Minh-son Phan, Etienne Baudrier, Loïc Mazo, Mohamed Tajine
    Abstract:

    This paper introduces a new method for estimating the Angular Difference between two tomographic projections belonging to a set of projections taken at unknown directions in 2D and 3D. Our method relies on the projection neighbor selection in projection moment space, the calculation of the Angular Differences between these neighboring projections using moment properties and a projection moment neighborhood graph. The accuracy and the robustness of our method are shown on a test database including fifty 2D and 3D gray-level images at different resolutions and with different levels of noise.

  • ICIP - Estimation of Angular Difference between tomographic projections taken at unknown directions in 3D
    2015 IEEE International Conference on Image Processing (ICIP), 2015
    Co-Authors: Minh-son Phan, Etienne Baudrier, Loïc Mazo, Mohamed Tajine
    Abstract:

    This paper deals with the estimation of Angular Difference between two tomographic projections belonging to a set of projections taken at unknown directions. The proposed method extends our former work from 2D to 3D. The method is potential for many applications such as projection refinement or projection classification, which are important in the process of tomographic reconstruction. Unlike to common line based Angular estimation, the proposed method does not need reference projections. Our method relies on the selection of projection neighbors with local adaptive thresholds, the calculus of the Angular Difference for neighboring projections by using properties of moments. The accuracy and the robustness of our method are shown on a test database including 50 3D gray-level images at different resolutions and with different levels of noise.

  • Angular Difference measure between tomographic projections taken at unknown directions in 2D
    2014
    Co-Authors: Minh-son Phan, Etienne Baudrier, Loïc Mazo, Mohamed Tajine
    Abstract:

    This paper introduces a new measure for estimating the Angular Difference between two tomographic projections belonging to a set of projections taken at unknown directions. The measure is potential for many applications such as projection refinement or projection classification, which are important in the process of tomographic reconstruction. Our measure relies on the construction of a neighborhood graph for projection moments, the calculus of the Angular Difference for neighboring projections and the computation of geodesics on this graph. The accuracy and the robustness of our measure is shown on a test database including 50 2D gray-level images at different resolutions and with different levels of noise.

  • ICIP - Angular Difference measure between tomographic projections taken at unknown directions in 2D
    2014 IEEE International Conference on Image Processing (ICIP), 2014
    Co-Authors: Minh-son Phan, Etienne Baudrier, Loïc Mazo, Mohamed Tajine
    Abstract:

    This paper introduces a new measure for estimating the Angular Difference between two tomographic projections belonging to a set of projections taken at unknown directions. The measure is potential for many applications such as projection refinement or projection classification, which are important in the process of tomographic reconstruction. Our measure relies on the construction of a neighborhood graph for projection moments, the calculus of the Angular Difference for neighboring projections and the computation of geodesics on this graph. The accuracy and the robustness of our measure is shown on a test database including 50 2D gray-level images at different resolutions and with different levels of noise.