A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper. First, the proposed Chin...A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper. First, the proposed Chinese character learning model uses the semanties of loeal context and global context to learn the representation of Chinese characters. Then, Chinese word segmentation model is built by a neural network, while the segmentation model is trained with the eharaeter representations as its input features. Finally, experimental results show that Chinese charaeter representations can effectively learn the semantic information. Characters with similar semantics cluster together in the visualize space. Moreover, the proposed Chinese word segmentation model also achieves a pretty good improvement on precision, recall and f-measure.展开更多
Recent convolutional neural networks(CNNs)based deep learning has significantly promoted fire detection.Existing fire detection methods can efficiently recognize and locate the fire.However,the accurate flame boundary...Recent convolutional neural networks(CNNs)based deep learning has significantly promoted fire detection.Existing fire detection methods can efficiently recognize and locate the fire.However,the accurate flame boundary and shape information is hard to obtain by them,which makes it difficult to conduct automated fire region analysis,prediction,and early warning.To this end,we propose a fire semantic segmentation method based on Global Position Guidance(GPG)and Multi-path explicit Edge information Interaction(MEI).Specifically,to solve the problem of local segmentation errors in low-level feature space,a top-down global position guidance module is used to restrain the offset of low-level features.Besides,an MEI module is proposed to explicitly extract and utilize the edge information to refine the coarse fire segmentation results.We compare the proposed method with existing advanced semantic segmentation and salient object detection methods.Experimental results demonstrate that the proposed method achieves 94.1%,93.6%,94.6%,95.3%,and 95.9%Intersection over Union(IoU)on five test sets respectively which outperforms the suboptimal method by a large margin.In addition,in terms of accuracy,our approach also achieves the best score.展开更多
A conventional global contrast enhancement is difficult to apply in various images because image quality and contrast enhancement are dependent on image characteristics largely. And a local contrast enhancement not on...A conventional global contrast enhancement is difficult to apply in various images because image quality and contrast enhancement are dependent on image characteristics largely. And a local contrast enhancement not only causes a washed-out effect, but also blocks. To solve these drawbacks, this paper derives an optimal global equalization function with variable size block based local contrast enhancement. The optimal equalization function makes it possible to get a good quality image through the global contrast enhancement. The variable size block segmentation is firstly exeoated using intensity differences as a measure of similarity. In the second step, the optimal global equalization function is obtained from the enhanced contrast image having variable size blocks. Conformed experiments have showed that the proposed algorithm produces a visually comfortable result image.展开更多
针对超声图像中肾脏、肾实质和肾窦自动分割存在的内部和边缘分割不准确问题,提出了一种用于肾脏超声图像分割的深度学习方法(GL-Unet11)。通过加深Unet网络,使网络具有更强的表达能力,并提出一个新的通道注意力网络——全局局部网络(GL...针对超声图像中肾脏、肾实质和肾窦自动分割存在的内部和边缘分割不准确问题,提出了一种用于肾脏超声图像分割的深度学习方法(GL-Unet11)。通过加深Unet网络,使网络具有更强的表达能力,并提出一个新的通道注意力网络——全局局部网络(GL-Net),该网络综合考虑了全局通道和局部通道对预测每个通道重要性的影响,使网络更好地关注重要信息。同时,将提出的全局局部网络加入到加深的Unet下采样部分的卷积块中,有效地增强了重要通道的特征。通过创建3个数据集,分别进行肾脏、肾实质和肾窦分割实验。实验结果表明,本方法在分割肾脏任务中,Dice系数达到96.25%,交并比(intersection over union,IOU)达到92.78%;在分割肾实质任务中,Dice系数达到92.90%,IOU达到86.75%;在分割肾窦任务中,Dice系数达到90.18%,IOU达到82.12%,分割结果均优于其它几种深度学习方法。该研究具有一定的创新性及医学应用价值。展开更多
为了对灰度不均匀图像进行分割,结合图像的局部和全局信息,提出基于加权L^(1)-L^(2)拟合能量项的模糊活动轮廓图像分割(fuzzy active contour based on weighted L^(1)-L^(2) fitting energy for image segmentation,FAWFE)模型。首先,...为了对灰度不均匀图像进行分割,结合图像的局部和全局信息,提出基于加权L^(1)-L^(2)拟合能量项的模糊活动轮廓图像分割(fuzzy active contour based on weighted L^(1)-L^(2) fitting energy for image segmentation,FAWFE)模型。首先,利用模糊隶属度函数,基于图像局部和全局信息构建混合模糊拟合图像。然后,构建加权L 1拟合能量项,量化原始图像与混合模糊拟合图像之间的差异,有效地处理灰度不均匀问题。最后,结合L^(2)拟合能量项,保证能量泛函的凸性,使得模型解的存在性和唯一性得以保证,避免陷入局部极小。另外,采用交替迭代算法对FAWFE模型进行数值求解,并与经典的活动轮廓模型对比。结果表明,FAWFE模型不仅可以准确定位目标边界,而且可以在0.6 s左右实现对图像的处理,对于合成图像和真实图像均有良好的处理效果。展开更多
针对肝脏肿瘤分割面临的病灶形状、大小和位置差异明显等问题,文章提出了一种基于空间通道注意力的三维肝脏肿瘤分割方法。在3D U-Net的基础上融合了Transformer,提出成对全局和局部注意力PGLA(Paired Global Local Attention)模块替代T...针对肝脏肿瘤分割面临的病灶形状、大小和位置差异明显等问题,文章提出了一种基于空间通道注意力的三维肝脏肿瘤分割方法。在3D U-Net的基础上融合了Transformer,提出成对全局和局部注意力PGLA(Paired Global Local Attention)模块替代Transformer中的传统注意力模块,并在尺度变换前引入CBAM(Convolutional Block Attention Module)模块。在肝脏肿瘤分割挑战赛数据集上的实验结果显示该方法在肿瘤分割的Dice系数上达到了69.18%,这些成绩均优于当前流行的模型,这证明了该方法在提高肝脏肿瘤分割精度方面的有效性。展开更多
基金Supported by the National Natural Science Foundation of China(No.61303179,U1135005,61175020)
文摘A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper. First, the proposed Chinese character learning model uses the semanties of loeal context and global context to learn the representation of Chinese characters. Then, Chinese word segmentation model is built by a neural network, while the segmentation model is trained with the eharaeter representations as its input features. Finally, experimental results show that Chinese charaeter representations can effectively learn the semantic information. Characters with similar semantics cluster together in the visualize space. Moreover, the proposed Chinese word segmentation model also achieves a pretty good improvement on precision, recall and f-measure.
基金This work was supported in part by the Important Science and Technology Project of Hainan Province under Grant ZDKJ2020010in part by Frontier Exploration Project Independently Deployed by Institute of Acoustics,Chinese Academy of Sciences under Grant QYTS202015 and Grant QYTS202115.
文摘Recent convolutional neural networks(CNNs)based deep learning has significantly promoted fire detection.Existing fire detection methods can efficiently recognize and locate the fire.However,the accurate flame boundary and shape information is hard to obtain by them,which makes it difficult to conduct automated fire region analysis,prediction,and early warning.To this end,we propose a fire semantic segmentation method based on Global Position Guidance(GPG)and Multi-path explicit Edge information Interaction(MEI).Specifically,to solve the problem of local segmentation errors in low-level feature space,a top-down global position guidance module is used to restrain the offset of low-level features.Besides,an MEI module is proposed to explicitly extract and utilize the edge information to refine the coarse fire segmentation results.We compare the proposed method with existing advanced semantic segmentation and salient object detection methods.Experimental results demonstrate that the proposed method achieves 94.1%,93.6%,94.6%,95.3%,and 95.9%Intersection over Union(IoU)on five test sets respectively which outperforms the suboptimal method by a large margin.In addition,in terms of accuracy,our approach also achieves the best score.
文摘A conventional global contrast enhancement is difficult to apply in various images because image quality and contrast enhancement are dependent on image characteristics largely. And a local contrast enhancement not only causes a washed-out effect, but also blocks. To solve these drawbacks, this paper derives an optimal global equalization function with variable size block based local contrast enhancement. The optimal equalization function makes it possible to get a good quality image through the global contrast enhancement. The variable size block segmentation is firstly exeoated using intensity differences as a measure of similarity. In the second step, the optimal global equalization function is obtained from the enhanced contrast image having variable size blocks. Conformed experiments have showed that the proposed algorithm produces a visually comfortable result image.
文摘针对超声图像中肾脏、肾实质和肾窦自动分割存在的内部和边缘分割不准确问题,提出了一种用于肾脏超声图像分割的深度学习方法(GL-Unet11)。通过加深Unet网络,使网络具有更强的表达能力,并提出一个新的通道注意力网络——全局局部网络(GL-Net),该网络综合考虑了全局通道和局部通道对预测每个通道重要性的影响,使网络更好地关注重要信息。同时,将提出的全局局部网络加入到加深的Unet下采样部分的卷积块中,有效地增强了重要通道的特征。通过创建3个数据集,分别进行肾脏、肾实质和肾窦分割实验。实验结果表明,本方法在分割肾脏任务中,Dice系数达到96.25%,交并比(intersection over union,IOU)达到92.78%;在分割肾实质任务中,Dice系数达到92.90%,IOU达到86.75%;在分割肾窦任务中,Dice系数达到90.18%,IOU达到82.12%,分割结果均优于其它几种深度学习方法。该研究具有一定的创新性及医学应用价值。
文摘为了对灰度不均匀图像进行分割,结合图像的局部和全局信息,提出基于加权L^(1)-L^(2)拟合能量项的模糊活动轮廓图像分割(fuzzy active contour based on weighted L^(1)-L^(2) fitting energy for image segmentation,FAWFE)模型。首先,利用模糊隶属度函数,基于图像局部和全局信息构建混合模糊拟合图像。然后,构建加权L 1拟合能量项,量化原始图像与混合模糊拟合图像之间的差异,有效地处理灰度不均匀问题。最后,结合L^(2)拟合能量项,保证能量泛函的凸性,使得模型解的存在性和唯一性得以保证,避免陷入局部极小。另外,采用交替迭代算法对FAWFE模型进行数值求解,并与经典的活动轮廓模型对比。结果表明,FAWFE模型不仅可以准确定位目标边界,而且可以在0.6 s左右实现对图像的处理,对于合成图像和真实图像均有良好的处理效果。
文摘针对肝脏肿瘤分割面临的病灶形状、大小和位置差异明显等问题,文章提出了一种基于空间通道注意力的三维肝脏肿瘤分割方法。在3D U-Net的基础上融合了Transformer,提出成对全局和局部注意力PGLA(Paired Global Local Attention)模块替代Transformer中的传统注意力模块,并在尺度变换前引入CBAM(Convolutional Block Attention Module)模块。在肝脏肿瘤分割挑战赛数据集上的实验结果显示该方法在肿瘤分割的Dice系数上达到了69.18%,这些成绩均优于当前流行的模型,这证明了该方法在提高肝脏肿瘤分割精度方面的有效性。
