基于遗传算法的自适应图像分割技术研究
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摘要
在数字人重建中,图像分割是数字人重建的关键要素,常规图像分割方法不但效率低,丢失信息严重,更重要的是分割精度极低。为了解决这些问题,提出了一种基于非局部相似正则化降噪方法的改进并将改进双种群遗传算法,通过分割结果表明论文提出的算法具有较高稳定性,分割效果较精确,而且大幅度降低了遗传算法的计算复杂度。
Image segmentation is a key factor in reestablishing digital human. The traditional image segmentation method is inefficient and has severe information loss. What's more,the segmentation accuracy is highly low. In order to solve these problems,an improved 2-population genetic algorithm based on non-local similarity regularization is proposed. The segmentation results show that the algorithm has high stability and accurate segmentation effect,and greatly reduces computation complexity of the genetic algorithm.
Image segmentation is a key factor in reestablishing digital human. The traditional image segmentation method is inefficient and has severe information loss. What's more,the segmentation accuracy is highly low. In order to solve these problems,an improved 2-population genetic algorithm based on non-local similarity regularization is proposed. The segmentation results show that the algorithm has high stability and accurate segmentation effect,and greatly reduces computation complexity of the genetic algorithm.
引文
[1]Sica F,Reale D,Poggi G,et al.Nonlocal Adaptive Multilooking in SAR Multipass Differential Interferometry[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015,8(4):1727-1742.
[2]Long Y,Li H C.Hyperspectral image classification by sparse representation with nonlocal adaptive dictionary[C]//2015 IEEE International Geoscience and Remote Sensing Symposium(IGARSS).IEEE,2015:1721-1724.
[3]Dong W,Zhang L,Shi G,et al.Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization[J].IEEE Transactions on Image Processing,2011,20(7):1838-1857.
[5]M.Protter,I.Yavneh,M.Elad.Closed-form MMSE estimation for signal denoising under sparse representation modeling over a unitary dictionary[J].IEEE Trans.Signal Process.,2010,58(7):3471-3484.
[6]A.M.Bruckstein,D.L.Donoho,M.Elad.From sparse solutions of systems of equations to sparse modeling of signals and images[J].SIAM Rev.,2009,51(1):34-81.
[7]Shao L,Yan R,Li X,et al.From heuristic optimization to dictionary learning:a review and comprehensive comparison of image denoising algorithms[J].Cybernetics,IEEE Transactions on,2014,44(7):1001-1013.
[8]Dong W,Zhang L,Shi G,et al.Nonlocally centralized sparse representation for image restoration[J].IEEETransactions on Image Processing,2013,22(4):1620-1630.
[9]Dong W,Zhang L,Lukac R,et al.Sparse representation based image interpolation with nonlocal autoregressive modeling[J].IEEE Transactions on Image Processing,2013,22(4):1382-1394.
[10]Ghamisi P,Benediktsson J A.Feature selection based on hybridization of genetic algorithm and particle swarm optimization[J].IEEE Geoscience and Remote Sensing Let-1000ters,2015,12(2):309-313.
[11]Van Wieren E,Kidwell K M,Desta Z,et al.Assessment of a clinical and genetic algorithm to predict endoxifen concentration[C]//ASCO Annual Meeting Proceedings,2016,34(15_suppl):e12021.
[17]Mukesh K G,Khanna H P,Velvizhi R V.An anomaly based Intrusion Detection System for mobile ad-hoc networks using genetic algorithm based support vector machine[J].Advances in Natural and Applied Sciences,2015,9(12):40-45.
[2]Long Y,Li H C.Hyperspectral image classification by sparse representation with nonlocal adaptive dictionary[C]//2015 IEEE International Geoscience and Remote Sensing Symposium(IGARSS).IEEE,2015:1721-1724.
[3]Dong W,Zhang L,Shi G,et al.Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization[J].IEEE Transactions on Image Processing,2011,20(7):1838-1857.
[5]M.Protter,I.Yavneh,M.Elad.Closed-form MMSE estimation for signal denoising under sparse representation modeling over a unitary dictionary[J].IEEE Trans.Signal Process.,2010,58(7):3471-3484.
[6]A.M.Bruckstein,D.L.Donoho,M.Elad.From sparse solutions of systems of equations to sparse modeling of signals and images[J].SIAM Rev.,2009,51(1):34-81.
[7]Shao L,Yan R,Li X,et al.From heuristic optimization to dictionary learning:a review and comprehensive comparison of image denoising algorithms[J].Cybernetics,IEEE Transactions on,2014,44(7):1001-1013.
[8]Dong W,Zhang L,Shi G,et al.Nonlocally centralized sparse representation for image restoration[J].IEEETransactions on Image Processing,2013,22(4):1620-1630.
[9]Dong W,Zhang L,Lukac R,et al.Sparse representation based image interpolation with nonlocal autoregressive modeling[J].IEEE Transactions on Image Processing,2013,22(4):1382-1394.
[10]Ghamisi P,Benediktsson J A.Feature selection based on hybridization of genetic algorithm and particle swarm optimization[J].IEEE Geoscience and Remote Sensing Let-1000ters,2015,12(2):309-313.
[11]Van Wieren E,Kidwell K M,Desta Z,et al.Assessment of a clinical and genetic algorithm to predict endoxifen concentration[C]//ASCO Annual Meeting Proceedings,2016,34(15_suppl):e12021.
[17]Mukesh K G,Khanna H P,Velvizhi R V.An anomaly based Intrusion Detection System for mobile ad-hoc networks using genetic algorithm based support vector machine[J].Advances in Natural and Applied Sciences,2015,9(12):40-45.