The Experts below are selected from a list of 386655 Experts worldwide ranked by ideXlab platform
Ma Xingmin - One of the best experts on this subject based on the ideXlab platform.
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Fast Bi-dimensional Empirical Mode based Multisource Image Fusion Decomposition
2019 28th Wireless and Optical Communications Conference (WOCC), 2019Co-Authors: Wang Huijuan, Jiang Yong, Ma XingminAbstract:Bi-dimensional empirical mode decomposition can decompose the source image into several Bi-dimensional Intrinsic Mode Functions. In the process of image decomposition, Interpolation is needed and the upper and lower envelopes will be drawn. However, these Interpolations and the drawings of upper and lower envelopes require a lot of computation time and manual screening. This paper proposes a simple but effective method that can maintain the characteristics of the original BEMD method, and the Hermite Interpolation reconstruction method is used to replace the surface Interpolation, and the variable neighborhood window method is used to replace the fixed neighborhood window method. We call it fast bi-dimensional empirical mode decomposition of the variable neighborhood window method based on research characteristics, and we finally complete the image fusion. The empirical analysis shows that this method can overcome the shortcomings that the source image features and details information of BIMF component decomposed from the original BEMD method are not rich enough, and reduce the calculation time, and the fusion quality is better.
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WOCC - Fast Bi-dimensional Empirical Mode based Multisource Image Fusion Decomposition
2019 28th Wireless and Optical Communications Conference (WOCC), 2019Co-Authors: Wang Hui-juan, Jiang Yong, Ma XingminAbstract:Bi-dimensional empirical mode decomposition can decompose the source image into several Bi-dimensional Intrinsic Mode Functions. In the process of image decomposition, Interpolation is needed and the upper and lower envelopes will be drawn. However, these Interpolations and the drawings of upper and lower envelopes require a lot of computation time and manual screening. This paper proposes a simple but effective method that can maintain the characteristics of the original BEMD method, and the Hermite Interpolation reconstruction method is used to replace the surface Interpolation, and the variable neighborhood window method is used to replace the fixed neighborhood window method. We call it fast bi-dimensional empirical mode decomposition of the variable neighborhood window method based on research characteristics, and we finally complete the image fusion. The empirical analysis shows that this method can overcome the shortcomings that the source image features and details information of BIMF component decomposed from the original BEMD method are not rich enough, and reduce the calculation time, and the fusion quality is better.
Chishyan Liaw - One of the best experts on this subject based on the ideXlab platform.
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SMC - A piecewise linear convolution Interpolation with third-order approximation for real-time image processing
2010 IEEE International Conference on Systems Man and Cybernetics, 2010Co-Authors: Chishyan Liaw, Ching-tsorng TsaiAbstract:This paper presents a high-performance architecture of a piecewise linear convolution Interpolation for digital image. The kernel of the proposed method is built up of piecewise linear polynomial and approximates the ideal sinc-function in interval [−2, 2]. The proposed architecture reduces the computational complexity of generating weighting coefficients and provides a simple hardware architecture design, low computation cost and is easy to meet real-time requirement. The architecture is implemented on the Virtex-II FPGA, and the VLSI architecture has been successfully designed and implemented with TSMC 0.13µm standard cell library. The simulation results indicate that the Interpolation quality of the proposed architecture is better than cubic convolution Interpolations mostly, which is able to process various-ratio image scaling for HDTV in real-time.
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A piecewise linear convolution Interpolation with third-order approximation for real-time image processing
2010 IEEE International Conference on Systems Man and Cybernetics, 2010Co-Authors: Chishyan Liaw, Ching-tsorng TsaiAbstract:This paper presents a high-performance architecture of a piecewise linear convolution Interpolation for digital image. The kernel of the proposed method is built up of piecewise linear polynomial and approximates the ideal sinc-function in interval [-2, 2]. The proposed architecture reduces the computational complexity of generating weighting coefficients and provides a simple hardware architecture design, low computation cost and is easy to meet real-time requirement. The architecture is implemented on the Virtex-II FPGA, and the VLSI architecture has been successfully designed and implemented with TSMC 0.13μm standard cell library. The simulation results indicate that the Interpolation quality of the proposed architecture is better than cubic convolution Interpolations mostly, which is able to process various-ratio image scaling for HDTV in real-time.
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Image reconstruction by convolution with piecewise linear polynomal kernel
2009 7th International Conference on Information Communications and Signal Processing (ICICS), 2009Co-Authors: Chishyan Liaw, Ming-hwa SheuAbstract:A superior Interpolation technique does not cause interpolated images distortion, nor does need complex computation. This paper presents a novel convolution Interpolation for digital image reconstruction. The kernel of the piecewise linear convolution Interpolation is built up of piecewise linear polynomial and approximates the ideal sinc-function in interval. The approach reduces the computational complexity of Interpolation and the simulation results indicate that the Interpolation quality is better than cubic convolution Interpolations mostly.
Ming-hwa Sheu - One of the best experts on this subject based on the ideXlab platform.
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Image reconstruction by convolution with piecewise linear polynomal kernel
2009 7th International Conference on Information Communications and Signal Processing (ICICS), 2009Co-Authors: Chishyan Liaw, Ming-hwa SheuAbstract:A superior Interpolation technique does not cause interpolated images distortion, nor does need complex computation. This paper presents a novel convolution Interpolation for digital image reconstruction. The kernel of the piecewise linear convolution Interpolation is built up of piecewise linear polynomial and approximates the ideal sinc-function in interval. The approach reduces the computational complexity of Interpolation and the simulation results indicate that the Interpolation quality is better than cubic convolution Interpolations mostly.
Jiang Yong - One of the best experts on this subject based on the ideXlab platform.
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Fast Bi-dimensional Empirical Mode based Multisource Image Fusion Decomposition
2019 28th Wireless and Optical Communications Conference (WOCC), 2019Co-Authors: Wang Huijuan, Jiang Yong, Ma XingminAbstract:Bi-dimensional empirical mode decomposition can decompose the source image into several Bi-dimensional Intrinsic Mode Functions. In the process of image decomposition, Interpolation is needed and the upper and lower envelopes will be drawn. However, these Interpolations and the drawings of upper and lower envelopes require a lot of computation time and manual screening. This paper proposes a simple but effective method that can maintain the characteristics of the original BEMD method, and the Hermite Interpolation reconstruction method is used to replace the surface Interpolation, and the variable neighborhood window method is used to replace the fixed neighborhood window method. We call it fast bi-dimensional empirical mode decomposition of the variable neighborhood window method based on research characteristics, and we finally complete the image fusion. The empirical analysis shows that this method can overcome the shortcomings that the source image features and details information of BIMF component decomposed from the original BEMD method are not rich enough, and reduce the calculation time, and the fusion quality is better.
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WOCC - Fast Bi-dimensional Empirical Mode based Multisource Image Fusion Decomposition
2019 28th Wireless and Optical Communications Conference (WOCC), 2019Co-Authors: Wang Hui-juan, Jiang Yong, Ma XingminAbstract:Bi-dimensional empirical mode decomposition can decompose the source image into several Bi-dimensional Intrinsic Mode Functions. In the process of image decomposition, Interpolation is needed and the upper and lower envelopes will be drawn. However, these Interpolations and the drawings of upper and lower envelopes require a lot of computation time and manual screening. This paper proposes a simple but effective method that can maintain the characteristics of the original BEMD method, and the Hermite Interpolation reconstruction method is used to replace the surface Interpolation, and the variable neighborhood window method is used to replace the fixed neighborhood window method. We call it fast bi-dimensional empirical mode decomposition of the variable neighborhood window method based on research characteristics, and we finally complete the image fusion. The empirical analysis shows that this method can overcome the shortcomings that the source image features and details information of BIMF component decomposed from the original BEMD method are not rich enough, and reduce the calculation time, and the fusion quality is better.
Christopher L. Bennett - One of the best experts on this subject based on the ideXlab platform.
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Missing Sample Recovery for Wireless Inertial Sensor-Based Human Movement Acquisition
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016Co-Authors: Vibhor Agrawal, Ignacio Gaunaurd, Robert S. Gailey, Christopher L. BennettAbstract:This paper presents a novel, practical, and effective routine to reconstruct missing samples from a time-domain sequence of wirelessly transmitted IMU data during high-level mobility activities. Our work extends previous approaches involving empirical mode decomposition (EMD)-based and auto-regressive (AR) model-based Interpolation algorithms in two aspects: 1) we utilized a modified sifting process for signal decomposition into a set of intrinsic mode functions with missing samples, and 2) we expand previous AR modeling for recovery of audio signals to exploit the quasi-periodic characteristics of lower-limb movement during the modified Edgren side step test. To verify the improvements provided by the proposed extensions, a comparison study of traditional Interpolation methods, such as cubic spline Interpolation, AR model-based Interpolations, and EMD-based Interpolation is also made via simulation with real inertial signals recorded during high-speed movement. The evaluation was based on two performance criteria: Euclidian distance and Pearson correlation coefficient between the original signal and the reconstructed signal. The experimental results show that the proposed method improves upon traditional Interpolation methods used in recovering missing samples.
