针对高光谱遥感图像分类中空间信息利用不充分、样本标记数量不足的问题,提出一种基于多尺度3D-CNN和卷积块注意力机制的高光谱图像分类方法。采用特征映射方式从不同感受野充分挖掘并融合高光谱图像的空间特征和光谱特征,对融合后的空...针对高光谱遥感图像分类中空间信息利用不充分、样本标记数量不足的问题,提出一种基于多尺度3D-CNN和卷积块注意力机制的高光谱图像分类方法。采用特征映射方式从不同感受野充分挖掘并融合高光谱图像的空间特征和光谱特征,对融合后的空谱特征进行卷积块注意力机制处理;通过残差思想构建深层网络,采用Dropout方法处理过拟合问题,最后通过Softmax分类器进行分类。在Indian Pines、Pavia University和Salinas Valley 3个高光谱数据集上进行大量实验,分类结果表明:所提方法优于其他经典方法。展开更多
针对卷积神经网络在高光谱图像特征提取和分类的过程中,存在空谱特征提取不充分以及网络层数太多引起的参数量大、计算复杂的问题,提出快速三维卷积神经网络(3D-CNN)结合深度可分离卷积(DSC)的轻量型卷积模型。该方法首先利用增量主成...针对卷积神经网络在高光谱图像特征提取和分类的过程中,存在空谱特征提取不充分以及网络层数太多引起的参数量大、计算复杂的问题,提出快速三维卷积神经网络(3D-CNN)结合深度可分离卷积(DSC)的轻量型卷积模型。该方法首先利用增量主成分分析(IPCA)对输入的数据进行降维预处理;其次将输入模型的像素分割成小的重叠的三维小卷积块,在分割的小块上基于中心像素形成地面标签,利用三维核函数进行卷积处理,形成连续的三维特征图,保留空谱特征。用3D-CNN同时提取空谱特征,然后在三维卷积中加入深度可分离卷积对空间特征再次提取,丰富空谱特征的同时减少参数量,从而减少计算时间,分类精度也有所提高。所提模型在Indian Pines、Salinas Scene和University of Pavia公开数据集上验证,并且同其他经典的分类方法进行比较。实验结果表明,该方法不仅能大幅度节省可学习的参数,降低模型复杂度,而且表现出较好的分类性能,其中总体精度(OA)、平均分类精度(AA)和Kappa系数均可达99%以上。展开更多
Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream.Many researchers have been working on visionbase...Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream.Many researchers have been working on visionbased gesture recognition due to its various applications.This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network(3D-CNN)and a Long Short-Term Memory(LSTM)network.The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation.The 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried out.The proposed model is a light-weight architecture with only 3.7 million training parameters.The model has been evaluated on 15 classes from the 20BN-jester dataset available publicly.The model was trained on 2000 video-clips per class which were separated into 80%training and 20%validation sets.An accuracy of 99%and 97%was achieved on training and testing data,respectively.We further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2+LSTM.展开更多
In lung nodules there is a huge variation in structural properties like Shape, Surface Texture. Even the spatial properties vary, where they can be found attached to lung walls, blood vessels in complex non-homogenous...In lung nodules there is a huge variation in structural properties like Shape, Surface Texture. Even the spatial properties vary, where they can be found attached to lung walls, blood vessels in complex non-homogenous lung structures. Moreover, the nodules are of small size at their early stage of development. This poses a serious challenge to develop a Computer aided diagnosis (CAD) system with better false positive reduction. Hence, to reduce the false positives per scan and to deal with the challenges mentioned, this paper proposes a set of three diverse 3D Attention based CNN architectures (3D ACNN) whose predictions on given low dose Volumetric Computed Tomography (CT) scans are fused to achieve more effective and reliable results. Attention mechanism is employed to selectively concentrate/weigh more on nodule specific features and less weight age over other irrelevant features. By using this attention based mechanism in CNN unlike traditional methods there was a significant gain in the classification performance. Contextual dependencies are also taken into account by giving three patches of different sizes surrounding the nodule as input to the ACNN architectures. The system is trained and validated using a publicly available LUNA16 dataset in a 10 fold cross validation approach where a competition performance metric (CPM) score of 0.931 is achieved. The experimental results demonstrate that either a single patch or a single architecture in a one-to-one fashion that is adopted in earlier methods cannot achieve a better performance and signifies the necessity of fusing different multi patched architectures. Though the proposed system is mainly designed for pulmonary nodule detection it can be easily extended to classification tasks of any other 3D medical diagnostic computed tomography images where there is a huge variation and uncertainty in classification.展开更多
肺癌是长期威胁人类健康的恶性疾病之一,针对传统方法在肺癌CT图像分类中的预处理过程复杂、工作量大的问题,本文提出了基于三维卷积神经网络(3D-CNN)模型的肺部CT图像分类方法。该模型以卷积神经网络模型为基础,并在训练的过程中使用...肺癌是长期威胁人类健康的恶性疾病之一,针对传统方法在肺癌CT图像分类中的预处理过程复杂、工作量大的问题,本文提出了基于三维卷积神经网络(3D-CNN)模型的肺部CT图像分类方法。该模型以卷积神经网络模型为基础,并在训练的过程中使用特定顺序输入策略,还在公开的Kaggle Data Science Bowl 2017数据集上进行了实验。实验表明,该方法对图像的分类准确率达到76%,比采用随机顺序的输入策略时有所提升,能够为肺部病理图像的分类研究提供有价值的参考。展开更多
为解决传统基于静态功能网络连接的自闭症分类算法忽略了脑功能连接的时变特性问题,提出一种基于膨胀卷积网络(inflated three dimension convolution neural network,I3D-CNN)的自闭症分类识别方法。提取被试大脑的静息态功能核磁共振...为解决传统基于静态功能网络连接的自闭症分类算法忽略了脑功能连接的时变特性问题,提出一种基于膨胀卷积网络(inflated three dimension convolution neural network,I3D-CNN)的自闭症分类识别方法。提取被试大脑的静息态功能核磁共振影像(rest state functional magnetic resonance imaging,RS-fMRI)每个感兴趣区域(region of interest,ROI)的时间序列,基于时间序列利用随机滑动时间窗口法,构建多个3D动态脑功能连接矩阵,使用I3D-CNN从3D动态脑功能连接矩阵中提取大脑的时空特征,建立自闭症分类模型。通过在ABIDE数据集上进行实验,验证了该方法的可行性和有效性。展开更多
文摘针对高光谱遥感图像分类中空间信息利用不充分、样本标记数量不足的问题,提出一种基于多尺度3D-CNN和卷积块注意力机制的高光谱图像分类方法。采用特征映射方式从不同感受野充分挖掘并融合高光谱图像的空间特征和光谱特征,对融合后的空谱特征进行卷积块注意力机制处理;通过残差思想构建深层网络,采用Dropout方法处理过拟合问题,最后通过Softmax分类器进行分类。在Indian Pines、Pavia University和Salinas Valley 3个高光谱数据集上进行大量实验,分类结果表明:所提方法优于其他经典方法。
文摘针对卷积神经网络在高光谱图像特征提取和分类的过程中,存在空谱特征提取不充分以及网络层数太多引起的参数量大、计算复杂的问题,提出快速三维卷积神经网络(3D-CNN)结合深度可分离卷积(DSC)的轻量型卷积模型。该方法首先利用增量主成分分析(IPCA)对输入的数据进行降维预处理;其次将输入模型的像素分割成小的重叠的三维小卷积块,在分割的小块上基于中心像素形成地面标签,利用三维核函数进行卷积处理,形成连续的三维特征图,保留空谱特征。用3D-CNN同时提取空谱特征,然后在三维卷积中加入深度可分离卷积对空间特征再次提取,丰富空谱特征的同时减少参数量,从而减少计算时间,分类精度也有所提高。所提模型在Indian Pines、Salinas Scene和University of Pavia公开数据集上验证,并且同其他经典的分类方法进行比较。实验结果表明,该方法不仅能大幅度节省可学习的参数,降低模型复杂度,而且表现出较好的分类性能,其中总体精度(OA)、平均分类精度(AA)和Kappa系数均可达99%以上。
文摘Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream.Many researchers have been working on visionbased gesture recognition due to its various applications.This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network(3D-CNN)and a Long Short-Term Memory(LSTM)network.The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation.The 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried out.The proposed model is a light-weight architecture with only 3.7 million training parameters.The model has been evaluated on 15 classes from the 20BN-jester dataset available publicly.The model was trained on 2000 video-clips per class which were separated into 80%training and 20%validation sets.An accuracy of 99%and 97%was achieved on training and testing data,respectively.We further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2+LSTM.
文摘In lung nodules there is a huge variation in structural properties like Shape, Surface Texture. Even the spatial properties vary, where they can be found attached to lung walls, blood vessels in complex non-homogenous lung structures. Moreover, the nodules are of small size at their early stage of development. This poses a serious challenge to develop a Computer aided diagnosis (CAD) system with better false positive reduction. Hence, to reduce the false positives per scan and to deal with the challenges mentioned, this paper proposes a set of three diverse 3D Attention based CNN architectures (3D ACNN) whose predictions on given low dose Volumetric Computed Tomography (CT) scans are fused to achieve more effective and reliable results. Attention mechanism is employed to selectively concentrate/weigh more on nodule specific features and less weight age over other irrelevant features. By using this attention based mechanism in CNN unlike traditional methods there was a significant gain in the classification performance. Contextual dependencies are also taken into account by giving three patches of different sizes surrounding the nodule as input to the ACNN architectures. The system is trained and validated using a publicly available LUNA16 dataset in a 10 fold cross validation approach where a competition performance metric (CPM) score of 0.931 is achieved. The experimental results demonstrate that either a single patch or a single architecture in a one-to-one fashion that is adopted in earlier methods cannot achieve a better performance and signifies the necessity of fusing different multi patched architectures. Though the proposed system is mainly designed for pulmonary nodule detection it can be easily extended to classification tasks of any other 3D medical diagnostic computed tomography images where there is a huge variation and uncertainty in classification.
文摘肺癌是长期威胁人类健康的恶性疾病之一,针对传统方法在肺癌CT图像分类中的预处理过程复杂、工作量大的问题,本文提出了基于三维卷积神经网络(3D-CNN)模型的肺部CT图像分类方法。该模型以卷积神经网络模型为基础,并在训练的过程中使用特定顺序输入策略,还在公开的Kaggle Data Science Bowl 2017数据集上进行了实验。实验表明,该方法对图像的分类准确率达到76%,比采用随机顺序的输入策略时有所提升,能够为肺部病理图像的分类研究提供有价值的参考。
文摘为解决传统基于静态功能网络连接的自闭症分类算法忽略了脑功能连接的时变特性问题,提出一种基于膨胀卷积网络(inflated three dimension convolution neural network,I3D-CNN)的自闭症分类识别方法。提取被试大脑的静息态功能核磁共振影像(rest state functional magnetic resonance imaging,RS-fMRI)每个感兴趣区域(region of interest,ROI)的时间序列,基于时间序列利用随机滑动时间窗口法,构建多个3D动态脑功能连接矩阵,使用I3D-CNN从3D动态脑功能连接矩阵中提取大脑的时空特征,建立自闭症分类模型。通过在ABIDE数据集上进行实验,验证了该方法的可行性和有效性。
