The Experts below are selected from a list of 30 Experts worldwide ranked by ideXlab platform
Zou Ben-qiang - One of the best experts on this subject based on the ideXlab platform.
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Various properties of symmetric Matrix and anti-symmetric Matrix
Journal of Shandong Institute of Light Industry, 2020Co-Authors: Zou Ben-qiangAbstract:Matrix serves as a key tool in the study of Advanced Algebra.However in studying the Transpose of Matrix much focus has been given on the definition of symmetric Matrix and anti-symmetric Matrix while their properties have not been fully explored.As special forms of Matrixes,symmetric and anti-symmetric Matrix plays a key role not only in the theory of Matrix but also in actual application.Properties of the two forms of special Matrixes are frequently used and discussed in the study of Matrix and related mathematical knowledge.This paper first presents the definition of symmetric Matrix and anti-symmetric Matrix and then moves on the disscussion of certain properties of them.
Onder Eyecioglu - One of the best experts on this subject based on the ideXlab platform.
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Performance Comparison Between OpenCV Built in CPU and GPU Functions on Image Processing Operations
arXiv: Distributed Parallel and Cluster Computing, 2019Co-Authors: Batuhan Hangun, Onder EyeciogluAbstract:Image Processing is a specialized area of Digital Signal Processing which contains various mathematical and algebraic operations such as Matrix inversion, Transpose of Matrix, derivative, convolution, Fourier Transform etc. Operations like those require higher computational capabilities than daily usage purposes of computers. At that point, with increased image sizes and more complex operations, CPUs may be unsatisfactory since they use Serial Processing by default. GPUs are the solution that come up with greater speed compared to CPUs because of their Parallel Processing/Computation nature. A parallel computing platform and programming model named CUDA was created by NVIDIA and implemented by the graphics processing units (GPUs) which were produced by them. In this paper, computing performance of some commonly used Image Processing operations will be compared on OpenCV's built in CPU and GPU functions that use CUDA.
Han Chengde - One of the best experts on this subject based on the ideXlab platform.
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Two-dimensional image processing without Transpose
Proceedings 7th International Conference on Signal Processing 2004. Proceedings. ICSP '04. 2004., 2020Co-Authors: Tu Baozhao, Li Dong, Han ChengdeAbstract:This paper provides a new solution to the cache efficiency problem in processing large two-dimensional image at both row and column directions, by implementing a special SDRAM controller in FPGA which can access image data at both directions efficiently and by employing some accessing rules in software which can guarantee cache coherence. By contrast with traditional corner turn (Transpose of Matrix) method, our design consumes less hardware resources by making additional corner turn memory unnecessary, and has the processing time shortened by skipping the comer turn phase.
Batuhan Hangun - One of the best experts on this subject based on the ideXlab platform.
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Performance Comparison Between OpenCV Built in CPU and GPU Functions on Image Processing Operations
arXiv: Distributed Parallel and Cluster Computing, 2019Co-Authors: Batuhan Hangun, Onder EyeciogluAbstract:Image Processing is a specialized area of Digital Signal Processing which contains various mathematical and algebraic operations such as Matrix inversion, Transpose of Matrix, derivative, convolution, Fourier Transform etc. Operations like those require higher computational capabilities than daily usage purposes of computers. At that point, with increased image sizes and more complex operations, CPUs may be unsatisfactory since they use Serial Processing by default. GPUs are the solution that come up with greater speed compared to CPUs because of their Parallel Processing/Computation nature. A parallel computing platform and programming model named CUDA was created by NVIDIA and implemented by the graphics processing units (GPUs) which were produced by them. In this paper, computing performance of some commonly used Image Processing operations will be compared on OpenCV's built in CPU and GPU functions that use CUDA.
Tu Baozhao - One of the best experts on this subject based on the ideXlab platform.
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Two-dimensional image processing without Transpose
Proceedings 7th International Conference on Signal Processing 2004. Proceedings. ICSP '04. 2004., 2020Co-Authors: Tu Baozhao, Li Dong, Han ChengdeAbstract:This paper provides a new solution to the cache efficiency problem in processing large two-dimensional image at both row and column directions, by implementing a special SDRAM controller in FPGA which can access image data at both directions efficiently and by employing some accessing rules in software which can guarantee cache coherence. By contrast with traditional corner turn (Transpose of Matrix) method, our design consumes less hardware resources by making additional corner turn memory unnecessary, and has the processing time shortened by skipping the comer turn phase.
