摘要
精确的图像分割是完成图像中物体姿态、大小估计的重要步骤,但由于物体的多样性、复杂性等原因,使得图像分割在计算机视觉领域仍然是具有挑战性的任务。针对标准U-Net模型实现端到端的图像分割时精确度不高、训练难以收敛等问题设计了一种基于Gloabl-Local评估方法的U-Net图像分割方法。首先根据同时兼顾全局信息和局部信息能够得到精确的图像分割图,论文提出了Gloabl-Local训练过程易拟合等问题,提出了改进U-Net法和4个公共显著性检测数据集训练改进U-Net网络模型,大大提高了图像分割的准确率。论文的方法平均准确率达到90.74%,与标准U-Net相比具有更好的分割效果。此方法实现了准确高效的图像分割,为估计图像中物体的姿态、大小提供了可靠依据。
Accurate image segmentation is an important step to complete the estimation of object pose and size in images. However,due to the diversity and complexity of objects,image segmentation is still a challenging task in the field of computer vision.According to the inaccuracy of the standard U-Net model in image segmentation and the difficulty of convergence in training,this paper designs a U-Net image segmentation method based on Gloabl-Local evaluation method. First,accurate image segmentation can be obtained according to both global information and local information. This paper proposes a Gloabl-Local evaluation method,which combines global information and local information. Secondly,aiming at the problem of easy fitting of standard U-Net training process,an improved U-Net network model is proposed,which solves the problem of over-fitting in the training process. Gloabl-Local evaluation method and four common saliency detection data sets are used to train the improved U-Net network model. The results show that this method greatly improves the accuracy of image segmentation. The average accuracy of the proposed method reaches 90.74%. This method has higher segmentation accuracy than standard U-Net. This method achieves accurate and efficient image segmentation and provides a reliable basis for estimating the pose and size of objects in the image.
引文
[1]Otsu N.A Threshold Selection Method from Gray-Level Histograms[J].IEEE Transactions on Systems,Man,and Cybernetics,1979,9(1):62-66.
[2]Lakshmi S,Sankaranarayanan D V.A study of edge detection techniques for segmentation computing approaches[J].IJCA Special Issue on“Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications”CASCT,2010:35-40.
[3]Adams R,Bischof L.Seeded region growing[J].IEEETransactions on pattern analysis and machine intelligence,1994,16(6):641-647.
[4]Selver M A,Kocao?lu A,Demir G K,et al.Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation[J].Computers in Biology and Medicine,2008,38(7):765-784.
[5]黄琴波.结合特定理论的图像分割方法[J].电子科技,2010,23(12):92-95.HUANG Qinbo.Image segmentation Method Cobined with Specific Theory[J].Electronic Technology,2010,23(12):92-95.
[6]Long J,Shelhamer E,Darrell T.Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2015:3431-3440.
[7]Chen L C,Papandreou G,Kokkinos I,et al.Deeplab:Semantic image segmentation with deep convolutional nets,atrous convolution,and fully connected crfs[J].IEEEtransactions on pattern analysis and machine intelligence,2018,40(4):834-848.
[8]Lin G,Shen C,Reid I,et al.Deeply learning the messages in message passing inference[C]//Advances in Neural Information Processing Systems.2015:361-369.
[9]Shore J,Johnson R.Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy[J].IEEE Transactions on information theory,1980,26(1):26-37.
[10]Arbelaez P,Maire M,Fowlkes C,et al.Contour detection and hierarchical image segmentation[J].IEEE transactions on pattern analysis and machine intelligence,2011,33(5):898-916.
[11]Jaccard P.étude comparative de la distribution florale dans une portion des Alpes et des Jura[J].Bull Soc Vaudoise Sci Nat,1901,37:547-579.
[12]Benesty J,Chen J,Huang Y,et al.Pearson correlation coefficient[M].Noise reduction in speech processing.Springer,Berlin,Heidelberg,2009:1-4.943
[13]Ronneberger O,Fischer P,Brox T.U-net:Convolutional networks for biomedical image segmentation[C]//International Conference on Medical image computing and computer-assisted intervention.Springer,Cham,2015:234-241.
[14]Wang Z,Bovik A C,Sheikh H R,et al.Image quality assessment:from error visibility to structural similarity[J].IEEE transactions on image processing,2004,13(4):600-612.
[15]Rokach L,Maimon O.Clustering methods[M].Data mining and knowledge discovery handbook.Springer,Boston,MA,2005:321-352.
[16]Han B,Zhu H,Ding Y.Bottom-up saliency based on weighted sparse coding residual[C]//Proceedings of the19th ACM international conference on Multimedia.ACM,2011:1117-1120.
[17]Pfeiffenberger H,Carlson D."Earth System Science Data"(ESSD)-A Peer Reviewed Journal for Publication of Data[J].D-Lib Magazine,2011,17(1/2).
[18]Tong N,Lu H,Ruan X,et al.Salient object detection via bootstrap learning[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2015:1884-1892.
[19]Li G,Yu Y.Visual saliency based on multiscale deep features[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2015:5455-5463.