GPU accelerated level set model solving by lattice boltzmann method with application to image segmentation
投稿时间:2019-11-06  修订日期:2020-09-04  download
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作者单位E-mail
石文君 空军工程大学航空机务士官学校, 河南 信阳 464000 shiwen_79_1@163.com 
王登位 航空航天学院, 电子科技大学, 四川 成都 611731
飞行器集群智能感知与协同控制四川省重点实验室,四川 成都 611731 
wdengwei@126.com 
刘万锁 空军工程大学航空机务士官学校, 河南 信阳 464000  
蒋大钢 航空航天学院, 电子科技大学, 四川 成都 611731
飞行器集群智能感知与协同控制四川省重点实验室,四川 成都 611731 
 
Abstract:A novel Graphics Processing Units (GPU) accelerated level set model which organically combines the global fitting energy and the local fitting energy from different models and the weighting coefficient of the global fitting term can be adaptively adjusted, is proposed to image segmentation. The proposed model can efficiently segment images with intensity inhomogeneity regardless of where the initial contour lies in the image. In its numerical implementation, an efficient numerical scheme called Lattice Boltzmann Method (LBM) is used to break the restrictions on time step. In addition, the proposed LSM is implemented by using a NVIDIA GPU to fully utilize the characteristics of LBM method with high parallelism. The extensive and promising experimental results from synthetic and real images demonstrate the effectiveness and efficiency of the proposed method.In addition, the factors that can have a key impact on segmentation performance are also analyzed in depth.
keywords:Intensity inhomogeneity  level set method  segmentation  Lattice Boltzmann method  graphics processing unit
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