Contraposing the need of the robust digital watermark for the copyright protection field, a new digital watermarking algorithm in the non-subsampled contourlet transform （NSCT） domain is proposed. The largest energy...Contraposing the need of the robust digital watermark for the copyright protection field, a new digital watermarking algorithm in the non-subsampled contourlet transform （NSCT） domain is proposed. The largest energy sub-band after NSCT is selected to embed watermark. The watermark is embedded into scaleinvariant feature transform （SIFT） regions. During embedding, the initial region is divided into some cirque sub-regions with the same area, and each watermark bit is embedded into one sub-region. Extensive simulation results and comparisons show that the algorithm gets a good trade-off of invisibility, robustness and capacity, thus obtaining good quality of the image while being able to effectively resist common image processing, and geometric and combo attacks, and normalized similarity is almost all reached.展开更多
A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low freq...A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.展开更多
针对在复杂场景下对弱纹理目标位姿估计的准确性和实时性问题,提出基于筛选学习网络的六自由度(6D)目标位姿估计算法。首先,将标准卷积替换为蓝图可分离卷积(BSConv)以减少模型参数,并使用GeLU(Gaussian error Linear Unit)激活函数,能...针对在复杂场景下对弱纹理目标位姿估计的准确性和实时性问题,提出基于筛选学习网络的六自由度(6D)目标位姿估计算法。首先,将标准卷积替换为蓝图可分离卷积(BSConv)以减少模型参数,并使用GeLU(Gaussian error Linear Unit)激活函数,能够更好地逼近正态分布,以提高网络模型的性能;其次,提出上采样筛选编码信息模块(UFAEM),弥补了上采样关键信息丢失的缺陷;最后,提出一种全局注意力机制(GAM),增加上下文信息,更有效地提取输入特征图的信息。在公开数据集LineMOD、YCB-Video和Occlusion LineMOD上测试,实验结果表明,所提算法在网络参数大幅度减少的同时提升了精度。所提算法网络参数量减少近3/4,采用ADD(-S) metric指标,在lineMOD数据集下较Dual-Stream算法精度提升约1.2个百分点,在YCB-Video数据集下较DenseFusion算法精度提升约5.2个百分点,在Occlusion LineMOD数据集下较像素投票网络(PVNet)算法精度提升约6.6个百分点。通过实验结果可知,所提算法对弱纹理目标位姿估计具有较好的效果,对遮挡物体位姿估计具有一定的鲁棒性。展开更多
Human pose estimation aims to localize the body joints from image or video data.With the development of deeplearning,pose estimation has become a hot research topic in the field of computer vision.In recent years,huma...Human pose estimation aims to localize the body joints from image or video data.With the development of deeplearning,pose estimation has become a hot research topic in the field of computer vision.In recent years,humanpose estimation has achieved great success in multiple fields such as animation and sports.However,to obtainaccurate positioning results,existing methods may suffer from large model sizes,a high number of parameters,and increased complexity,leading to high computing costs.In this paper,we propose a new lightweight featureencoder to construct a high-resolution network that reduces the number of parameters and lowers the computingcost.We also introduced a semantic enhancement module that improves global feature extraction and networkperformance by combining channel and spatial dimensions.Furthermore,we propose a dense connected spatialpyramid pooling module to compensate for the decrease in image resolution and information loss in the network.Finally,ourmethod effectively reduces the number of parameters and complexitywhile ensuring high performance.Extensive experiments show that our method achieves a competitive performance while dramatically reducing thenumber of parameters,and operational complexity.Specifically,our method can obtain 89.9%AP score on MPIIVAL,while the number of parameters and the complexity of operations were reduced by 41%and 36%,respectively.展开更多
使用骨盆X光片诊断发育性髋关节发育不良(Developmental Dysplasia of the Hip,DDH)要求准确地标注髋关节关键点,而深度学习方法能作为可靠的辅助工具。针对骨盆片拍摄姿势和拍摄距离多样化问题,本文基于U-Net提出了RKD-UNet来检测髋关...使用骨盆X光片诊断发育性髋关节发育不良(Developmental Dysplasia of the Hip,DDH)要求准确地标注髋关节关键点,而深度学习方法能作为可靠的辅助工具。针对骨盆片拍摄姿势和拍摄距离多样化问题,本文基于U-Net提出了RKD-UNet来检测髋关节关键点。该模型使用残差块改进U-Net的卷积层和skip-connection路径,并将坐标注意力引入到编码器中以增强模型对关键点邻域的特征提取能力。在编码器顶部使用卷积和ASPP模块构成Bridge块,以[3,6,9]的空洞率融合不同尺度的特征信息并提升模型的感受野。本文使用包含骨盆正位片、蛙位片、下肢全长片和术后骨盆片的数据集训练和测试模型。RKD-UNet实现了3.19±2.19 px的平均关键点检测误差和2.83°±2.59°的平均髋臼角测量误差。对正常、轻度、中度和重度脱位案例诊断的F1分数分别达到89.6、77.1、57.9和94.1,高于医生的手动诊断结果。实验结果表明,RKD-UNet能准确检测髋关节关键点并辅助医生诊断DDH。展开更多
基金supported by the National Natural Science Foundation of China(61379010)the Natural Science Basic Research Plan in Shaanxi Province of China(2015JM6293)
文摘Contraposing the need of the robust digital watermark for the copyright protection field, a new digital watermarking algorithm in the non-subsampled contourlet transform （NSCT） domain is proposed. The largest energy sub-band after NSCT is selected to embed watermark. The watermark is embedded into scaleinvariant feature transform （SIFT） regions. During embedding, the initial region is divided into some cirque sub-regions with the same area, and each watermark bit is embedded into one sub-region. Extensive simulation results and comparisons show that the algorithm gets a good trade-off of invisibility, robustness and capacity, thus obtaining good quality of the image while being able to effectively resist common image processing, and geometric and combo attacks, and normalized similarity is almost all reached.
基金supported by the National Natural Science Foundation of China (6117212711071002)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education (20113401110006)the Innovative Research Team of 211 Project in Anhui University (KJTD007A)
文摘A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.
文摘深度歧义是单帧图像多人3D姿态估计面临的重要挑战,提取图像上下文对缓解深度歧义极具潜力.自顶向下方法大多基于人体检测建模关键点关系,人体包围框粒度粗背景噪声占比较大,极易导致关键点偏移或误匹配,还将影响基于人体尺度因子估计绝对深度的可靠性.自底向上的方法直接检出图像中的人体关键点再逐一恢复3D人体姿态.虽然能够显式获取场景上下文,但在相对深度估计方面处于劣势.提出新的双分支网络,自顶向下分支基于关键点区域提议提取人体上下文,自底向上分支基于三维空间提取场景上下文.提出带噪声抑制的人体上下文提取方法,通过建模“关键点区域提议”描述人体目标,建模姿态关联的动态稀疏关键点关系剔除弱连接减少噪声传播.提出从鸟瞰视角提取场景上下文的方法,通过建模图像深度特征并映射鸟瞰平面获得三维空间人体位置布局;设计人体和场景上下文融合网络预测人体绝对深度.在公开数据集MuPoTS-3D和Human3.6M上的实验结果表明:与同类先进模型相比,所提模型HSC-Pose的相对和绝对3D关键点位置精度至少提高2.2%和0.5%;平均根关键点位置误差至少降低4.2 mm.
基金the National Natural Science Foundation of China(Grant Number 62076246).
文摘Human pose estimation aims to localize the body joints from image or video data.With the development of deeplearning,pose estimation has become a hot research topic in the field of computer vision.In recent years,humanpose estimation has achieved great success in multiple fields such as animation and sports.However,to obtainaccurate positioning results,existing methods may suffer from large model sizes,a high number of parameters,and increased complexity,leading to high computing costs.In this paper,we propose a new lightweight featureencoder to construct a high-resolution network that reduces the number of parameters and lowers the computingcost.We also introduced a semantic enhancement module that improves global feature extraction and networkperformance by combining channel and spatial dimensions.Furthermore,we propose a dense connected spatialpyramid pooling module to compensate for the decrease in image resolution and information loss in the network.Finally,ourmethod effectively reduces the number of parameters and complexitywhile ensuring high performance.Extensive experiments show that our method achieves a competitive performance while dramatically reducing thenumber of parameters,and operational complexity.Specifically,our method can obtain 89.9%AP score on MPIIVAL,while the number of parameters and the complexity of operations were reduced by 41%and 36%,respectively.
文摘使用骨盆X光片诊断发育性髋关节发育不良(Developmental Dysplasia of the Hip,DDH)要求准确地标注髋关节关键点,而深度学习方法能作为可靠的辅助工具。针对骨盆片拍摄姿势和拍摄距离多样化问题,本文基于U-Net提出了RKD-UNet来检测髋关节关键点。该模型使用残差块改进U-Net的卷积层和skip-connection路径,并将坐标注意力引入到编码器中以增强模型对关键点邻域的特征提取能力。在编码器顶部使用卷积和ASPP模块构成Bridge块,以[3,6,9]的空洞率融合不同尺度的特征信息并提升模型的感受野。本文使用包含骨盆正位片、蛙位片、下肢全长片和术后骨盆片的数据集训练和测试模型。RKD-UNet实现了3.19±2.19 px的平均关键点检测误差和2.83°±2.59°的平均髋臼角测量误差。对正常、轻度、中度和重度脱位案例诊断的F1分数分别达到89.6、77.1、57.9和94.1,高于医生的手动诊断结果。实验结果表明,RKD-UNet能准确检测髋关节关键点并辅助医生诊断DDH。
