A Real-Time Parallel Image Processing Approach on Regular PCs with Multi-Core CPUs

In this paper a parallel image processing and frame rate stabilization approach is proposed. This approach works on a regular PC with a multi-core CPU. It is implemented under .NET Framework and tested on Microsoft Windows 7 operating system, performing several experiments. It is also applied to a face recognition application to increase its image processing performance successfully. Results show that, handled workload when 4 physical cores are used is approximately 5.25 times the workload handled with one core. It is also shown that the approach successfully distributes the workload on CPU cores and produces output at a stable frame rate under both steady and unsteady workloads. This approach can be used for various signal processing or multimedia applications to parallelize their tasks to increase the performance on multi-core CPUs. DOI: http://dx.doi.org/10.5755/j01.eie.23.6.19696

[1]  Qingxiang Wu,et al.  GPU Implementation of Spiking Neural Networks for Edge Detection , 2013, ICIC.

[2]  Thambipillai Srikanthan,et al.  Parallelizing the Hough Transform Computation , 2008, IEEE Signal Processing Letters.

[3]  Sabine Pruggnaller,et al.  Performance evaluation of image processing algorithms on the GPU. , 2008, Journal of structural biology.

[4]  Jiawen Chen,et al.  Real-time edge-aware image processing with the bilateral grid , 2007, SIGGRAPH 2007.

[5]  Peter Messmer,et al.  GPU‐Accelerated Sparse Matrix–Matrix Multiplication for Linear Scaling Density Functional Theory , 2016 .

[6]  Julien Langou,et al.  A Class of Parallel Tiled Linear Algebra Algorithms for Multicore Architectures , 2007, Parallel Comput..

[7]  Jan Bartovsky,et al.  GPU implementation of linear morphological openings with arbitrary angle , 2012, Journal of Real-Time Image Processing.

[8]  Bernard Tourancheau,et al.  Multi-GPU implementation of the lattice Boltzmann method , 2013, Comput. Math. Appl..

[9]  Daijin Kim,et al.  Robust Real-Time Face Detection Using Face Certainty Map , 2007, ICB.

[10]  Mahesh B Fattepur,et al.  PROCESSING VIDEOS USING PARALLEL COMPUTING: A NOVEL APPROACH , 2015 .

[11]  R. Buhrman,et al.  GPU-accelerated micromagnetic simulations using cloud computing , 2015, 1505.01207.

[12]  Mircea Andrecut,et al.  Parallel GPU Implementation of Iterative PCA Algorithms , 2008, J. Comput. Biol..

[13]  Yakup Genc,et al.  GPU-based Video Feature Tracking And Matching , 2006 .

[14]  Yinghai Lu,et al.  Multicore parallel min-cost flow algorithm for CAD applications , 2009, 2009 46th ACM/IEEE Design Automation Conference.