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Sparse Decomposition for Traffic Images Using Quantum-inspired Evolutionary Algorithms


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Sparse Decomposition for Traffic Images Using Quantum-inspired Evolutionary Algorithms
FENG Xiao-qiangHE Tie-jun
Intelligent Transportation System Research Center,Southeast University,Nanjing 210096,China
image processing traffic images sparse decomposition quantum-inspired evolutionary algorithms
To represent a traffic image in a flexible,sparse and adaptive way,this paper introduces the image sparse decomposition method into traffic image processing and presents a novel traffic image sparse decomposition approach based on quantum-inspired evolutionary algorithms to accelerate the processing speed of traffic image representation,which is helpful to extract traffic feature parameters from images.The sparse representation for traffic images is fulfilled by using nonsymmetrical image atoms to construct a traffic image atom dictionary,and employing the quantum-inspired evolutionary algorithm with strong search capability and rapid convergence to search the best image atom from an over-complete image atom dictionary to match the local structures of traffic images.Simulation experiments show that the introduced method can obtain a sparse decomposition of traffic images in a fast and effective way,which validates the approach presented here.


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Last Update: 2012-11-02