The Experts below are selected from a list of 10569 Experts worldwide ranked by ideXlab platform

Hamid Krim - One of the best experts on this subject based on the ideXlab platform.

  • Robust Subspace Clustering by Bi-Sparsity Pursuit: Guarantees and Sequential Algorithm
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Ashkan Panahi, Xiao Bian, Hamid Krim
    Abstract:

    We consider subspace clustering under sparse noise, for which a non-convex optimization framework based on sparse data representations has been recently developed. This setup is suitable for a large variety of applications with high dimensional data, such as image processing, which is naturally decomposed into a sparse unstructured foreground and a background residing in a union of low-dimensional subspaces. In this framework, we further discuss both performance and implementation of the key optimization problem. We provide an analysis of this optimization problem demonstrating that our approach is capable of recovering linear subspaces as a local optimal solution for sufficiently large data sets and sparse noise vectors. We also propose a Sequential Algorithmic solution, which is particularly useful for extremely large data sets and online vision applications such as video processing.

  • WACV - Robust Subspace Clustering by Bi-Sparsity Pursuit: Guarantees and Sequential Algorithm
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Ashkan Panahi, Xiao Bian, Hamid Krim
    Abstract:

    We consider subspace clustering under sparse noise, for which a non-convex optimization framework based on sparse data representations has been recently developed. This setup is suitable for a large variety of applications with high dimensional data, such as image processing, which is naturally decomposed into a sparse unstructured foreground and a background residing in a union of low-dimensional subspaces. In this framework, we further discuss both performance and implementation of the key optimization problem. We provide an analysis of this optimization problem demonstrating that our approach is capable of recovering linear subspaces as a local optimal solution for sufficiently large data sets and sparse noise vectors. We also propose a Sequential Algorithmic solution, which is particularly useful for extremely large data sets and online vision applications such as video processing.

Ashkan Panahi - One of the best experts on this subject based on the ideXlab platform.

  • Robust Subspace Clustering by Bi-Sparsity Pursuit: Guarantees and Sequential Algorithm
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Ashkan Panahi, Xiao Bian, Hamid Krim
    Abstract:

    We consider subspace clustering under sparse noise, for which a non-convex optimization framework based on sparse data representations has been recently developed. This setup is suitable for a large variety of applications with high dimensional data, such as image processing, which is naturally decomposed into a sparse unstructured foreground and a background residing in a union of low-dimensional subspaces. In this framework, we further discuss both performance and implementation of the key optimization problem. We provide an analysis of this optimization problem demonstrating that our approach is capable of recovering linear subspaces as a local optimal solution for sufficiently large data sets and sparse noise vectors. We also propose a Sequential Algorithmic solution, which is particularly useful for extremely large data sets and online vision applications such as video processing.

  • WACV - Robust Subspace Clustering by Bi-Sparsity Pursuit: Guarantees and Sequential Algorithm
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Ashkan Panahi, Xiao Bian, Hamid Krim
    Abstract:

    We consider subspace clustering under sparse noise, for which a non-convex optimization framework based on sparse data representations has been recently developed. This setup is suitable for a large variety of applications with high dimensional data, such as image processing, which is naturally decomposed into a sparse unstructured foreground and a background residing in a union of low-dimensional subspaces. In this framework, we further discuss both performance and implementation of the key optimization problem. We provide an analysis of this optimization problem demonstrating that our approach is capable of recovering linear subspaces as a local optimal solution for sufficiently large data sets and sparse noise vectors. We also propose a Sequential Algorithmic solution, which is particularly useful for extremely large data sets and online vision applications such as video processing.

Yongwon Jang - One of the best experts on this subject based on the ideXlab platform.

  • EMBC - Sequential Algorithm for the detection of the shockable rhythms in electrocardiogram
    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2012
    Co-Authors: Ji-wook Jeong, Yoonseon Song, Yongwon Jang
    Abstract:

    We suggest a Sequential Algorithm for the detection of the ventricular fibrillation (VF) and ventricular tachycardia (VT) of a rate above 180 bpm, so called shockable rhythms. The built-in Algorithm for ECG analysis embedded in the portable bio-signal sensing module is aimed to discriminate between shockable and non-shockable rhythms and its accuracy is analyzed. An Algorithm for VF/VT detection is proposed to analyze every 1 s ECG episode using the past 8 s episodes. The method is tested with 844,587 ECG episodes from the widely accepted databases. A sensitivity of 86.8 % and a specificity of 99.4 % were obtained and compared with the previous results.

  • Sequential Algorithm for the detection of the shockable rhythms in electrocardiogram
    2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012
    Co-Authors: Ji-wook Jeong, Yoonseon Song, Yongwon Jang
    Abstract:

    We suggest a Sequential Algorithm for the detection of the ventricular fibrillation (VF) and ventricular tachycardia (VT) of a rate above 180 bpm, so called shockable rhythms. The built-in Algorithm for ECG analysis embedded in the portable bio-signal sensing module is aimed to discriminate between shockable and non-shockable rhythms and its accuracy is analyzed. An Algorithm for VF/VT detection is proposed to analyze every 1 s ECG episode using the past 8 s episodes. The method is tested with 844,587 ECG episodes from the widely accepted databases. A sensitivity of 86.8 % and a specificity of 99.4 % were obtained and compared with the previous results.

Xiao Bian - One of the best experts on this subject based on the ideXlab platform.

  • Robust Subspace Clustering by Bi-Sparsity Pursuit: Guarantees and Sequential Algorithm
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Ashkan Panahi, Xiao Bian, Hamid Krim
    Abstract:

    We consider subspace clustering under sparse noise, for which a non-convex optimization framework based on sparse data representations has been recently developed. This setup is suitable for a large variety of applications with high dimensional data, such as image processing, which is naturally decomposed into a sparse unstructured foreground and a background residing in a union of low-dimensional subspaces. In this framework, we further discuss both performance and implementation of the key optimization problem. We provide an analysis of this optimization problem demonstrating that our approach is capable of recovering linear subspaces as a local optimal solution for sufficiently large data sets and sparse noise vectors. We also propose a Sequential Algorithmic solution, which is particularly useful for extremely large data sets and online vision applications such as video processing.

  • WACV - Robust Subspace Clustering by Bi-Sparsity Pursuit: Guarantees and Sequential Algorithm
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Ashkan Panahi, Xiao Bian, Hamid Krim
    Abstract:

    We consider subspace clustering under sparse noise, for which a non-convex optimization framework based on sparse data representations has been recently developed. This setup is suitable for a large variety of applications with high dimensional data, such as image processing, which is naturally decomposed into a sparse unstructured foreground and a background residing in a union of low-dimensional subspaces. In this framework, we further discuss both performance and implementation of the key optimization problem. We provide an analysis of this optimization problem demonstrating that our approach is capable of recovering linear subspaces as a local optimal solution for sufficiently large data sets and sparse noise vectors. We also propose a Sequential Algorithmic solution, which is particularly useful for extremely large data sets and online vision applications such as video processing.

Ji-wook Jeong - One of the best experts on this subject based on the ideXlab platform.

  • EMBC - Sequential Algorithm for the detection of the shockable rhythms in electrocardiogram
    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2012
    Co-Authors: Ji-wook Jeong, Yoonseon Song, Yongwon Jang
    Abstract:

    We suggest a Sequential Algorithm for the detection of the ventricular fibrillation (VF) and ventricular tachycardia (VT) of a rate above 180 bpm, so called shockable rhythms. The built-in Algorithm for ECG analysis embedded in the portable bio-signal sensing module is aimed to discriminate between shockable and non-shockable rhythms and its accuracy is analyzed. An Algorithm for VF/VT detection is proposed to analyze every 1 s ECG episode using the past 8 s episodes. The method is tested with 844,587 ECG episodes from the widely accepted databases. A sensitivity of 86.8 % and a specificity of 99.4 % were obtained and compared with the previous results.

  • Sequential Algorithm for the detection of the shockable rhythms in electrocardiogram
    2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012
    Co-Authors: Ji-wook Jeong, Yoonseon Song, Yongwon Jang
    Abstract:

    We suggest a Sequential Algorithm for the detection of the ventricular fibrillation (VF) and ventricular tachycardia (VT) of a rate above 180 bpm, so called shockable rhythms. The built-in Algorithm for ECG analysis embedded in the portable bio-signal sensing module is aimed to discriminate between shockable and non-shockable rhythms and its accuracy is analyzed. An Algorithm for VF/VT detection is proposed to analyze every 1 s ECG episode using the past 8 s episodes. The method is tested with 844,587 ECG episodes from the widely accepted databases. A sensitivity of 86.8 % and a specificity of 99.4 % were obtained and compared with the previous results.