针对多视图立体网络在弱纹理或非朗伯曲面等挑战性区域重建效果差的问题,首先提出一个基于3个并行扩展卷积和注意力机制的多尺度特征提取模块,在增加感受野的同时捕获特征之间的依赖关系以获取全局上下文信息,从而提升多视图立体网络在...针对多视图立体网络在弱纹理或非朗伯曲面等挑战性区域重建效果差的问题,首先提出一个基于3个并行扩展卷积和注意力机制的多尺度特征提取模块,在增加感受野的同时捕获特征之间的依赖关系以获取全局上下文信息,从而提升多视图立体网络在挑战性区域特征的表征能力以进行鲁棒的特征匹配。其次在代价体正则化3D CNN部分引入注意力机制,使网络注意于代价体中的重要区域以进行平滑处理。另外建立一个神经渲染网络,该网络利用渲染参考损失精确地解析辐射场景表达的几何外观信息,并引入深度一致性损失保持多视图立体网络与神经渲染网络之间的几何一致性,有效地缓解有噪声代价体对多视图立体网络的不利影响。该算法在室内DTU数据集中测试,点云重建的完整性和整体性指标分别为0.289和0.326,与基准方法CasMVSNet相比,分别提升24.9%和8.2%,即使在挑战性区域也得到高质量的重建效果;在室外Tanks and Temples中级数据集中,点云重建的平均F-score为60.31,与方法UCS-Net相比提升9.9%,体现出较强的泛化能力。展开更多
旨在构建我国绵羊群体遗传结构的精细图谱,为我国绵羊遗传资源的保存、利用提供依据。利用乌珠穆沁羊、湖羊、同羊、大尾寒羊、罗布羊、哈萨克羊、多浪羊、迪庆羊、青海臧羊、四川藏羊、西藏藏羊共计11个地方绵羊品种(资源)的Illumina O...旨在构建我国绵羊群体遗传结构的精细图谱,为我国绵羊遗传资源的保存、利用提供依据。利用乌珠穆沁羊、湖羊、同羊、大尾寒羊、罗布羊、哈萨克羊、多浪羊、迪庆羊、青海臧羊、四川藏羊、西藏藏羊共计11个地方绵羊品种(资源)的Illumina Ovine SNP 50K芯片数据,应用NETVIEW、PCA、STRUCTURE、NJ树等方法对群体结构进行分析。结果表明,乌珠穆沁羊与除罗布羊以外的蒙古系绵羊均有直接遗传关系,与罗布羊有间接的遗传关系。罗布羊与哈萨克系绵羊有较近的遗传关系,而哈萨克系的哈萨克羊与多浪羊存在较远的遗传关系。迪庆羊能够与藏系绵羊分离开,西藏地区藏羊能够与其他地区的藏羊分开,青海、四川的藏羊不能分开。结果提示,NETVIEW计算时间短,且基本能够反映史实,因而可以作为未来群体结构分析的工具;乌珠穆沁羊是此次试验选用的蒙古系绵羊中最古老的品种;蒙古系的罗布羊因混有哈萨克系绵羊的血统而与新疆地区的绵羊聚在一起;不同地区的藏羊存在着分化趋势,但分化并不严重。展开更多
According to the characteristics of dynamic firing in pulse coupled neural network (PCNN) and regional configuration in retinal blood vessel network, a new method combined with simplified PCNN and fast 2D-Otsu algorit...According to the characteristics of dynamic firing in pulse coupled neural network (PCNN) and regional configuration in retinal blood vessel network, a new method combined with simplified PCNN and fast 2D-Otsu algorithm was proposed for automated retinal blood vessels segmentation. Firstly, 2D Gaussian matched filter was used to enhance the retinal images and simplified PCNN was employed to segment the blood vessels by firing neighborhood neurons. Then, fast 2D-Otsu algorithm was introduced to search the best segmentation results and iteration times with less computation time. Finally, the whole vessel network was obtained via analyzing the regional connectivity. Experiments implemented on the public Hoover database indicate that this new method gets a 0.803 5 true positive rate and a 0.028 0 false positive rate on an average. According to the test results, compared with Hoover algorithm and method of PCNN and 1D-Otsu, the proposed method shows much better performance.展开更多
A new vision-based approach was presented for predicting the behavior of the ball carrier—shooting, passing and dribbling in basketball matches. It was proposed to recognize the ball carrier’s head pose by classifyi...A new vision-based approach was presented for predicting the behavior of the ball carrier—shooting, passing and dribbling in basketball matches. It was proposed to recognize the ball carrier’s head pose by classifying its yaw angle to determine his vision range and the court situation of the sportsman within his vision range can be further learned. In basketball match videos characterized by cluttered background, fast motion of the sportsmen and low resolution of their head images, and the covariance descriptor, were adopted to fuse multiple visual features of the head region, which can be seen as a point on the Riemannian manifold and then mapped to the tangent space. Then, the classification of head yaw angle was directly completed in this space through the trained multiclass LogitBoost. In order to describe the court situation of all sportsmen within the ball carrier’s vision range, artificial potential field (APF)-based information was introduced. Finally, the behavior of the ball carrier—shooting, passing and dribbling, was predicted using radial basis function (RBF) neural network as the classifier. Experimental results show that the average prediction accuracy of the proposed method can reach 80% on the video recorded in basketball matches, which validates its effectiveness.展开更多
文摘针对多视图立体网络在弱纹理或非朗伯曲面等挑战性区域重建效果差的问题,首先提出一个基于3个并行扩展卷积和注意力机制的多尺度特征提取模块,在增加感受野的同时捕获特征之间的依赖关系以获取全局上下文信息,从而提升多视图立体网络在挑战性区域特征的表征能力以进行鲁棒的特征匹配。其次在代价体正则化3D CNN部分引入注意力机制,使网络注意于代价体中的重要区域以进行平滑处理。另外建立一个神经渲染网络,该网络利用渲染参考损失精确地解析辐射场景表达的几何外观信息,并引入深度一致性损失保持多视图立体网络与神经渲染网络之间的几何一致性,有效地缓解有噪声代价体对多视图立体网络的不利影响。该算法在室内DTU数据集中测试,点云重建的完整性和整体性指标分别为0.289和0.326,与基准方法CasMVSNet相比,分别提升24.9%和8.2%,即使在挑战性区域也得到高质量的重建效果;在室外Tanks and Temples中级数据集中,点云重建的平均F-score为60.31,与方法UCS-Net相比提升9.9%,体现出较强的泛化能力。
文摘旨在构建我国绵羊群体遗传结构的精细图谱,为我国绵羊遗传资源的保存、利用提供依据。利用乌珠穆沁羊、湖羊、同羊、大尾寒羊、罗布羊、哈萨克羊、多浪羊、迪庆羊、青海臧羊、四川藏羊、西藏藏羊共计11个地方绵羊品种(资源)的Illumina Ovine SNP 50K芯片数据,应用NETVIEW、PCA、STRUCTURE、NJ树等方法对群体结构进行分析。结果表明,乌珠穆沁羊与除罗布羊以外的蒙古系绵羊均有直接遗传关系,与罗布羊有间接的遗传关系。罗布羊与哈萨克系绵羊有较近的遗传关系,而哈萨克系的哈萨克羊与多浪羊存在较远的遗传关系。迪庆羊能够与藏系绵羊分离开,西藏地区藏羊能够与其他地区的藏羊分开,青海、四川的藏羊不能分开。结果提示,NETVIEW计算时间短,且基本能够反映史实,因而可以作为未来群体结构分析的工具;乌珠穆沁羊是此次试验选用的蒙古系绵羊中最古老的品种;蒙古系的罗布羊因混有哈萨克系绵羊的血统而与新疆地区的绵羊聚在一起;不同地区的藏羊存在着分化趋势,但分化并不严重。
基金Project (60872081) supported by the National Natural Science Foundation of ChinaProject (50051) supported by the Program for New Century Excellent Talents in UniversityProject (4092034) supported by the Natural Science Foundation of Beijing
文摘According to the characteristics of dynamic firing in pulse coupled neural network (PCNN) and regional configuration in retinal blood vessel network, a new method combined with simplified PCNN and fast 2D-Otsu algorithm was proposed for automated retinal blood vessels segmentation. Firstly, 2D Gaussian matched filter was used to enhance the retinal images and simplified PCNN was employed to segment the blood vessels by firing neighborhood neurons. Then, fast 2D-Otsu algorithm was introduced to search the best segmentation results and iteration times with less computation time. Finally, the whole vessel network was obtained via analyzing the regional connectivity. Experiments implemented on the public Hoover database indicate that this new method gets a 0.803 5 true positive rate and a 0.028 0 false positive rate on an average. According to the test results, compared with Hoover algorithm and method of PCNN and 1D-Otsu, the proposed method shows much better performance.
基金Project(50808025) supported by the National Natural Science Foundation of ChinaProject(20090162110057) supported by the Doctoral Fund of Ministry of Education, China
文摘A new vision-based approach was presented for predicting the behavior of the ball carrier—shooting, passing and dribbling in basketball matches. It was proposed to recognize the ball carrier’s head pose by classifying its yaw angle to determine his vision range and the court situation of the sportsman within his vision range can be further learned. In basketball match videos characterized by cluttered background, fast motion of the sportsmen and low resolution of their head images, and the covariance descriptor, were adopted to fuse multiple visual features of the head region, which can be seen as a point on the Riemannian manifold and then mapped to the tangent space. Then, the classification of head yaw angle was directly completed in this space through the trained multiclass LogitBoost. In order to describe the court situation of all sportsmen within the ball carrier’s vision range, artificial potential field (APF)-based information was introduced. Finally, the behavior of the ball carrier—shooting, passing and dribbling, was predicted using radial basis function (RBF) neural network as the classifier. Experimental results show that the average prediction accuracy of the proposed method can reach 80% on the video recorded in basketball matches, which validates its effectiveness.
