The Experts below are selected from a list of 1869 Experts worldwide ranked by ideXlab platform
Peng Gao - One of the best experts on this subject based on the ideXlab platform.
-
ICIG (2) - Joint Multi-frame Detection and Segmentation for Multi-Cell Tracking.
Lecture Notes in Computer Science, 2019Co-Authors: Zibin Zhou, Fei Wang, Huaying Chen, Peng GaoAbstract:Tracking living Cells in video sequence is difficult, because of Cell morphology and high similarities between Cells. Tracking-by-detection methods are widely used in multi-Cell tracking. We perform multi-Cell tracking based on the Cell Centroid detection, and the performance of the detector has high impact on tracking performance. In this paper, UNet is utilized to extract inter-frame and intra-frame spatio-temporal information of Cells. Detection performance of Cells in mitotic phase is improved by multi-frame input. Good detection results facilitate multi-Cell tracking. A mitosis detection algorithm is proposed to detect Cell mitosis and the Cell lineage is built up. Another UNet is utilized to acquire primary segmentation. Jointly using detection and primary segmentation, Cells can be fine segmented in highly dense Cell population. Experiments are conducted to evaluate the effectiveness of our method, and results show its state-of-the-art performance.
-
Joint Multi-frame Detection and Segmentation for Multi-Cell Tracking
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Zibin Zhou, Fei Wang, Huaying Chen, Peng GaoAbstract:Tracking living Cells in video sequence is difficult, because of Cell morphology and high similarities between Cells. Tracking-by-detection methods are widely used in multi-Cell tracking. We perform multi-Cell tracking based on the Cell Centroid detection, and the performance of the detector has high impact on tracking performance. In this paper, UNet is utilized to extract inter-frame and intra-frame spatio-temporal information of Cells. Detection performance of Cells in mitotic phase is improved by multi-frame input. Good detection results facilitate multi-Cell tracking. A mitosis detection algorithm is proposed to detect Cell mitosis and the Cell lineage is built up. Another UNet is utilized to acquire primary segmentation. Jointly using detection and primary segmentation, Cells can be fine segmented in highly dense Cell population. Experiments are conducted to evaluate the effectiveness of our method, and results show its state-of-the-art performance.
Zibin Zhou - One of the best experts on this subject based on the ideXlab platform.
-
ICIG (2) - Joint Multi-frame Detection and Segmentation for Multi-Cell Tracking.
Lecture Notes in Computer Science, 2019Co-Authors: Zibin Zhou, Fei Wang, Huaying Chen, Peng GaoAbstract:Tracking living Cells in video sequence is difficult, because of Cell morphology and high similarities between Cells. Tracking-by-detection methods are widely used in multi-Cell tracking. We perform multi-Cell tracking based on the Cell Centroid detection, and the performance of the detector has high impact on tracking performance. In this paper, UNet is utilized to extract inter-frame and intra-frame spatio-temporal information of Cells. Detection performance of Cells in mitotic phase is improved by multi-frame input. Good detection results facilitate multi-Cell tracking. A mitosis detection algorithm is proposed to detect Cell mitosis and the Cell lineage is built up. Another UNet is utilized to acquire primary segmentation. Jointly using detection and primary segmentation, Cells can be fine segmented in highly dense Cell population. Experiments are conducted to evaluate the effectiveness of our method, and results show its state-of-the-art performance.
-
Joint Multi-frame Detection and Segmentation for Multi-Cell Tracking
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Zibin Zhou, Fei Wang, Huaying Chen, Peng GaoAbstract:Tracking living Cells in video sequence is difficult, because of Cell morphology and high similarities between Cells. Tracking-by-detection methods are widely used in multi-Cell tracking. We perform multi-Cell tracking based on the Cell Centroid detection, and the performance of the detector has high impact on tracking performance. In this paper, UNet is utilized to extract inter-frame and intra-frame spatio-temporal information of Cells. Detection performance of Cells in mitotic phase is improved by multi-frame input. Good detection results facilitate multi-Cell tracking. A mitosis detection algorithm is proposed to detect Cell mitosis and the Cell lineage is built up. Another UNet is utilized to acquire primary segmentation. Jointly using detection and primary segmentation, Cells can be fine segmented in highly dense Cell population. Experiments are conducted to evaluate the effectiveness of our method, and results show its state-of-the-art performance.
Huai-bao Zhang - One of the best experts on this subject based on the ideXlab platform.
-
Extending the global-direction stencil with "face-area-weighted Centroid" to unstructured finite volume discretization from integral form
Advances in Aerodynamics, 2020Co-Authors: Ling-fa Kong, Yi-dao Dong, Wei Liu, Huai-bao ZhangAbstract:Accuracy of unstructured finite volume discretization is greatly influenced by the gradient reconstruction. For the commonly used k-exact reconstruction method, the Cell Centroid is always chosen as the reference point to formulate the reconstructed function. But in some practical problems, such as the boundary layer, Cells in this area are always set with high aspect ratio to improve the local field resolution, and if geometric Centroid is still utilized for the spatial discretization, the severe grid skewness cannot be avoided, which is adverse to the numerical performance of unstructured finite volume solver. In previous work [Kong, et al. Chin Phys B 29(10):100203, 2020], we explored a novel global-direction stencil and combined it with the face-area-weighted Centroid on unstructured finite volume methods from differential form to realize the skewness reduction and a better reflection of flow anisotropy. Greatly inspired by the differential form, in this research, we demonstrate that it is also feasible to extend this novel method to the unstructured finite volume discretization from integral form on both second and third-order finite volume solver. Numerical examples governed by linear convective, Euler and Laplacian equations are utilized to examine the correctness as well as effectiveness of this extension. Compared with traditional vertex-neighbor and face-neighbor stencils based on the geometric Centroid, the grid skewness is almost eliminated and computational accuracy as well as convergence rate is greatly improved by the global-direction stencil with face-area-weighted Centroid. As a result, on unstructured finite volume discretization from integral form, the method also has superiorities on both computational accuracy and convergence rate.
-
Extending the global-direction stencil with "face-area-weighted Centroid" to unstructured finite volume discretization from integral form
2020Co-Authors: Ling-fa Kong, Yi-dao Dong, Wei Liu, Huai-bao ZhangAbstract:Abstract Accuracy of unstructured finite volume discretization is greatly influenced by the gradient reconstruction. For the commonly used k-exact reconstruction method, the Cell Centroid is always chosen as the reference point to formulate the reconstructed function. But in some practical problems, such as the boundary layer, Cells in this area are always set with high aspect ratio to improve the local field resolution, and if geometric Centroid is still utilized for the spatial discretization, the severe grid skewness cannot be avoided, which is adverse to the numerical performance of unstructured finite volume solver. In previous work [Chinese Physics B. 2020, In press], we explored a novel global-direction stencil and combined it with the face-area-weighted Centroid on unstructured finite volume methods from differential form to realize the skewness reduction and a better reflection of flow anisotropy. Greatly inspired by the differential form, in this research, we demonstrate that it is also feasible to extend this novel method to the unstructured finite volume discretization from integral form on both second and third-order finite volume solver. Numerical examples governed by linear convective, Euler and Laplacian equations are utilized to examine the correctness as well as effectiveness of this extension. Compared with traditional vertex-neighbor and face-neighbor stencils based on the geometric Centroid, the grid skewness is almost eliminated and computational accuracy as well as convergence rate is greatly improved by the global-direction stencil with face-area-weighted Centroid. As a result, on unstructured finite volume discretization from integral form, the method also has superiorities on both computational accuracy and convergence rate.
-
Extending the global-direction stencil with "face-area-weighted Centroid" to unstructured finite volume discretization from integral form
2020Co-Authors: Ling-fa Kong, Yi-dao Dong, Wei Liu, Huai-bao ZhangAbstract:Abstract Accuracy of unstructured finite volume discretization is greatly influenced by the gradient reconstruction. For the commonly used k-exact reconstruction method, the Cell Centroid is always chosen as the reference point to formulate the reconstructed function. But in some practical problems, such as the boundary layer, Cells in this area are always set with high aspect ratio to improve the local field resolution, and if geometric Centroid is still utilized for the spatial discretization, the severe grid skewness cannot be avoided, which is adverse to the numerical performance of unstructured finite volume solver. In previous work, we explored a novel global-direction stencil and combine it with face-area-weighted Centroid on unstructured finite volume methods from differential form to realize the skewness reduction and a better reflection of flow anisotropy. Note, however, that the differential form is hard to achieve higher-order accuracy, and in order to set stage for the method promotion on higher-order numerical simulation, in this research, we demonstrate that it is also feasible to extend this novel method to the unstructured finite volume discretization in integral form. Numerical examples governed by linear convective, Euler and Laplacian equations are utilized to examine the correctness as well as effectiveness of this extension. Compared with traditional vertex-neighbor and face-neighbor stencils based on the geometric Centroid, the grid skewness is almost eliminated and computational accuracy as well as convergence rate is greatly improved by the global-direction stencil with face-area-weighted Centroid. As a result, on unstructured finite volume discretization from integral form, the method also has a better numerical performance.
Huaying Chen - One of the best experts on this subject based on the ideXlab platform.
-
ICIG (2) - Joint Multi-frame Detection and Segmentation for Multi-Cell Tracking.
Lecture Notes in Computer Science, 2019Co-Authors: Zibin Zhou, Fei Wang, Huaying Chen, Peng GaoAbstract:Tracking living Cells in video sequence is difficult, because of Cell morphology and high similarities between Cells. Tracking-by-detection methods are widely used in multi-Cell tracking. We perform multi-Cell tracking based on the Cell Centroid detection, and the performance of the detector has high impact on tracking performance. In this paper, UNet is utilized to extract inter-frame and intra-frame spatio-temporal information of Cells. Detection performance of Cells in mitotic phase is improved by multi-frame input. Good detection results facilitate multi-Cell tracking. A mitosis detection algorithm is proposed to detect Cell mitosis and the Cell lineage is built up. Another UNet is utilized to acquire primary segmentation. Jointly using detection and primary segmentation, Cells can be fine segmented in highly dense Cell population. Experiments are conducted to evaluate the effectiveness of our method, and results show its state-of-the-art performance.
-
Joint Multi-frame Detection and Segmentation for Multi-Cell Tracking
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Zibin Zhou, Fei Wang, Huaying Chen, Peng GaoAbstract:Tracking living Cells in video sequence is difficult, because of Cell morphology and high similarities between Cells. Tracking-by-detection methods are widely used in multi-Cell tracking. We perform multi-Cell tracking based on the Cell Centroid detection, and the performance of the detector has high impact on tracking performance. In this paper, UNet is utilized to extract inter-frame and intra-frame spatio-temporal information of Cells. Detection performance of Cells in mitotic phase is improved by multi-frame input. Good detection results facilitate multi-Cell tracking. A mitosis detection algorithm is proposed to detect Cell mitosis and the Cell lineage is built up. Another UNet is utilized to acquire primary segmentation. Jointly using detection and primary segmentation, Cells can be fine segmented in highly dense Cell population. Experiments are conducted to evaluate the effectiveness of our method, and results show its state-of-the-art performance.
Fei Wang - One of the best experts on this subject based on the ideXlab platform.
-
ICIG (2) - Joint Multi-frame Detection and Segmentation for Multi-Cell Tracking.
Lecture Notes in Computer Science, 2019Co-Authors: Zibin Zhou, Fei Wang, Huaying Chen, Peng GaoAbstract:Tracking living Cells in video sequence is difficult, because of Cell morphology and high similarities between Cells. Tracking-by-detection methods are widely used in multi-Cell tracking. We perform multi-Cell tracking based on the Cell Centroid detection, and the performance of the detector has high impact on tracking performance. In this paper, UNet is utilized to extract inter-frame and intra-frame spatio-temporal information of Cells. Detection performance of Cells in mitotic phase is improved by multi-frame input. Good detection results facilitate multi-Cell tracking. A mitosis detection algorithm is proposed to detect Cell mitosis and the Cell lineage is built up. Another UNet is utilized to acquire primary segmentation. Jointly using detection and primary segmentation, Cells can be fine segmented in highly dense Cell population. Experiments are conducted to evaluate the effectiveness of our method, and results show its state-of-the-art performance.
-
Joint Multi-frame Detection and Segmentation for Multi-Cell Tracking
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Zibin Zhou, Fei Wang, Huaying Chen, Peng GaoAbstract:Tracking living Cells in video sequence is difficult, because of Cell morphology and high similarities between Cells. Tracking-by-detection methods are widely used in multi-Cell tracking. We perform multi-Cell tracking based on the Cell Centroid detection, and the performance of the detector has high impact on tracking performance. In this paper, UNet is utilized to extract inter-frame and intra-frame spatio-temporal information of Cells. Detection performance of Cells in mitotic phase is improved by multi-frame input. Good detection results facilitate multi-Cell tracking. A mitosis detection algorithm is proposed to detect Cell mitosis and the Cell lineage is built up. Another UNet is utilized to acquire primary segmentation. Jointly using detection and primary segmentation, Cells can be fine segmented in highly dense Cell population. Experiments are conducted to evaluate the effectiveness of our method, and results show its state-of-the-art performance.