针对目前靶场炮弹火焰图像分割算法对火焰边界分割效果差而导致定位精度下降的问题,基于PSPNet算法,结合双向特征融合模块以及全注意力机制网络的特征金字塔转换模块,提出改进PSPNet的炮弹火焰分割PSP_FPT(pyramid scene parsing_featur...针对目前靶场炮弹火焰图像分割算法对火焰边界分割效果差而导致定位精度下降的问题,基于PSPNet算法,结合双向特征融合模块以及全注意力机制网络的特征金字塔转换模块,提出改进PSPNet的炮弹火焰分割PSP_FPT(pyramid scene parsing_feature pyramid with Transformer)算法,实现对炮弹火焰目标的高精度分割。利用双向特征融合模块对金字塔池化后的特征进行双向融合,增强各子区域以及全局目标空间和语义特征的相关性,提升炮弹火焰分割的准确性。为了避免火焰周围烟雾、扬尘对分割效果的影响,将特征金字塔转换模块与全注意力机制网络相结合,优化双向特征融合模块输出后的特征映射,提升区域内目标之间的空间结构关系;提高算法对炮弹火焰目标与背景干扰之间的辨别力,进一步提高算法的识别能力。将全局池化后的特征作为全注意力机制网络的输入,解决了由于图像输入序列过长导致全注意力机制网络参数量过大的问题,进而降低工程应用的实现难度。实验结果表明,该算法在炮弹火焰数据集上分割的平均交并比达98.01%,平均准确率达98.97%,对炮弹火焰分割有较强的鲁棒性和较高的准确率。展开更多
A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec-...A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.展开更多
Based on mass balance theory and IsoSource program,stable carbon and nitrogen isotopic ratios revealed that small mammals (plateau pika,root vole and plateau zokor) contributed 26.8% and 27.0% and 29.2% to alpine weas...Based on mass balance theory and IsoSource program,stable carbon and nitrogen isotopic ratios revealed that small mammals (plateau pika,root vole and plateau zokor) contributed 26.8% and 27.0% and 29.2% to alpine weasel,steppe polecat and upland buzzard of carnivores as food respectively;adult passerine birds contributed 22.3%,47.7% and 69.1%,with hatchlings contributing 50.9%,25.6% and 1.70% to each respectively.δ 13 C values plotted against δ 15 N indicated significant partitioning in two-dimensional space among the three carnivores.It was reasonable to propose a food resource partitioning among alpine weasel,steppe polecat and upland buzzard,which partially revealed their co-existence mechanisms.展开更多
Building segmentation from high-resolution synthetic aperture radar (SAR) images has always been one of the important research issues. Due to the existence of speckle noise and multipath effect, the pixel values chang...Building segmentation from high-resolution synthetic aperture radar (SAR) images has always been one of the important research issues. Due to the existence of speckle noise and multipath effect, the pixel values change drastically, causing the large intensity differences in pixels of building areas. Moreover, the geometric structure of buildings can cause strong scattering spots, which brings difficulties to the segmentation and extraction of buildings. To solve of these problems, this paper presents a coherence-coefficient-based Markov random field (CCMRF) approach for building segmentation from high-resolution SAR images. The method introduces the coherence coefficient of interferometric synthetic aperture radar (InSAR) into the neighborhood energy based on traditional Markov random field (MRF), which makes interferometric and spatial contextual information more fully used in SAR image segmentation. According to the Hammersley-Clifford theorem, the problem of maximum a posteriori (MAP) for image segmentation is transformed into the solution of minimizing the sum of likelihood energy and neighborhood energy. Finally, the iterative condition model (ICM) is used to find the optimal solution. The experimental results demonstrate that the proposed method can segment SAR building effectively and obtain more accurate results than the traditional MRF method and K-means clustering.展开更多
This paper presents a voxel-based region growing method for automatic road surface extraction from mobile laser scanning point clouds in an expressway environment.The proposed method has three major steps:constructing...This paper presents a voxel-based region growing method for automatic road surface extraction from mobile laser scanning point clouds in an expressway environment.The proposed method has three major steps:constructing a voxel model;extracting the road surface points by employing the voxel-based segmentation algorithm;refining the road boundary using the curb-based segmentation algorithm.To evaluate the accuracy of the proposed method,the two-point cloud datasets of two typical test sites in an expressway environment consisting of flat and bumpy surfaces with a high slope were used.The proposed algorithm extracted the road surface successfully with high accuracy.There was an average recall of 99.5%,the precision was 96.3%,and the F1 score was 97.9%.From the extracted road surface,a framework for the estimation of road roughness was proposed.Good agreement was achieved when comparing the results of the road roughness map with the visual image,indicating the feasibility and effectiveness of the proposed framework.展开更多
Using conventional Mean Shift Algorithm to segment high spatial resolution Remote sensing images of mountainous areas usually leads to an unsatisfactory result, due to its rich texture information. In this paper, we p...Using conventional Mean Shift Algorithm to segment high spatial resolution Remote sensing images of mountainous areas usually leads to an unsatisfactory result, due to its rich texture information. In this paper, we propose an improved Mean Shift Algorithm in consideration of the characteristics of these images. First, images were classified into several homogeneous color regions and texture regions by conducting variance detection on the color space. Next, each homogeneous color region was directly segmented to generate the preliminary results by applying the Mean Shift Algorithm. For each texture region, we conduct a high-dimensional feature space by extracting information such as color, texture and shape comprehensively, and work out a proper bandwidth according to the normalized distribution density. Then the bandwidth variable Mean Shift Algorithm was applied to obtain segmentation results by conducting the pattern classification in feature space. Last, the final results were obtained by merging these regions by means of the constructed cost functions and removing the oversegmented regions from the merged regions. It has been experimentally segmented on the high spatial resolution remote sensing images collected by Quickbird and Unmanned Aerial Vehicle(UAV). We put forward an approach to evaluate the segmentation results by using the segmentation matching index(SMI). This takes into consideration both the area and the spectrum. The experimental results suggest that the improved Mean Shift Algorithm outperforms the conventional one in terms of accuracy of segmentation.展开更多
A two-stage method for image segmentation based on edge and region information is proposed. Different deformation schemes are used at two stages for segmenting the object correctly in image plane. At the first stage, ...A two-stage method for image segmentation based on edge and region information is proposed. Different deformation schemes are used at two stages for segmenting the object correctly in image plane. At the first stage, the contour of the model is divided into several segments hierarchically that deform respectively using affine transformation. After the contour is deformed to the approximate boundary of object, a fine match mechanism using statistical information of local region to redefine the external energy of the model is used to make the contour fit the object's boundary exactly. The algorithm is effective, as the hierarchical segmental deformation makes use of the globe and local information of the image, the affine transformation keeps the consistency of the model, and the reformative approaches of computing the internal energy and external energy are proposed to reduce the algorithm complexity. The adaptive method of defining the search area at the second stage makes the model converge quickly. The experimental results indicate that the proposed model is effective and robust to local minima and able to search for concave objects.展开更多
This paper introduces the use of partition of unity method for the development of a high order finite volume discretization scheme on unstructured grids for solving diffusion models based on partial differential equat...This paper introduces the use of partition of unity method for the development of a high order finite volume discretization scheme on unstructured grids for solving diffusion models based on partial differential equations.The unknown function and its gradient can be accurately reconstructed using high order optimal recovery based on radial basis functions.The methodology proposed is applied to the noise removal problem in functional surfaces and images.Numerical results demonstrate the effectiveness of the new numerical approach and provide experimental order of convergence.展开更多
An improved approach for JSEG is presented for unsupervised segmentation of homogeneous regions in gray-scale images. Instead of intensity quantization, an automatic classification method based on scale space-based cl...An improved approach for JSEG is presented for unsupervised segmentation of homogeneous regions in gray-scale images. Instead of intensity quantization, an automatic classification method based on scale space-based clustering is used for nonparametric clustering of image data set. Then EM algorithm with classification achieved by space-based classification scheme as initial data used to achieve Gaussian mixture modelling of image data set that is utilized for the calculation of soft J value. Original region growing algorithm is then used to segment the image based on the multiscale soft J-images. Experiments show that the new method can overcome the limitations of JSEG successfully.展开更多
文摘针对目前靶场炮弹火焰图像分割算法对火焰边界分割效果差而导致定位精度下降的问题,基于PSPNet算法,结合双向特征融合模块以及全注意力机制网络的特征金字塔转换模块,提出改进PSPNet的炮弹火焰分割PSP_FPT(pyramid scene parsing_feature pyramid with Transformer)算法,实现对炮弹火焰目标的高精度分割。利用双向特征融合模块对金字塔池化后的特征进行双向融合,增强各子区域以及全局目标空间和语义特征的相关性,提升炮弹火焰分割的准确性。为了避免火焰周围烟雾、扬尘对分割效果的影响,将特征金字塔转换模块与全注意力机制网络相结合,优化双向特征融合模块输出后的特征映射,提升区域内目标之间的空间结构关系;提高算法对炮弹火焰目标与背景干扰之间的辨别力,进一步提高算法的识别能力。将全局池化后的特征作为全注意力机制网络的输入,解决了由于图像输入序列过长导致全注意力机制网络参数量过大的问题,进而降低工程应用的实现难度。实验结果表明,该算法在炮弹火焰数据集上分割的平均交并比达98.01%,平均准确率达98.97%,对炮弹火焰分割有较强的鲁棒性和较高的准确率。
基金Supported by the National Natural Science Foundation of China(60505004,60773061)~~
文摘A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.
文摘Based on mass balance theory and IsoSource program,stable carbon and nitrogen isotopic ratios revealed that small mammals (plateau pika,root vole and plateau zokor) contributed 26.8% and 27.0% and 29.2% to alpine weasel,steppe polecat and upland buzzard of carnivores as food respectively;adult passerine birds contributed 22.3%,47.7% and 69.1%,with hatchlings contributing 50.9%,25.6% and 1.70% to each respectively.δ 13 C values plotted against δ 15 N indicated significant partitioning in two-dimensional space among the three carnivores.It was reasonable to propose a food resource partitioning among alpine weasel,steppe polecat and upland buzzard,which partially revealed their co-existence mechanisms.
文摘Building segmentation from high-resolution synthetic aperture radar (SAR) images has always been one of the important research issues. Due to the existence of speckle noise and multipath effect, the pixel values change drastically, causing the large intensity differences in pixels of building areas. Moreover, the geometric structure of buildings can cause strong scattering spots, which brings difficulties to the segmentation and extraction of buildings. To solve of these problems, this paper presents a coherence-coefficient-based Markov random field (CCMRF) approach for building segmentation from high-resolution SAR images. The method introduces the coherence coefficient of interferometric synthetic aperture radar (InSAR) into the neighborhood energy based on traditional Markov random field (MRF), which makes interferometric and spatial contextual information more fully used in SAR image segmentation. According to the Hammersley-Clifford theorem, the problem of maximum a posteriori (MAP) for image segmentation is transformed into the solution of minimizing the sum of likelihood energy and neighborhood energy. Finally, the iterative condition model (ICM) is used to find the optimal solution. The experimental results demonstrate that the proposed method can segment SAR building effectively and obtain more accurate results than the traditional MRF method and K-means clustering.
基金Project(SIIT-AUN/SEED-Net-G-S1 Y16/018)supported by the Doctoral Asean University Network ProgramProject supported by the Metropolitan Expressway Co.,Ltd.,Japan+2 种基金Project supported by Elysium Co.Ltd.Project supported by Aero Asahi Corporation,Co.,Ltd.Project supported by the Expressway Authority of Thailand。
文摘This paper presents a voxel-based region growing method for automatic road surface extraction from mobile laser scanning point clouds in an expressway environment.The proposed method has three major steps:constructing a voxel model;extracting the road surface points by employing the voxel-based segmentation algorithm;refining the road boundary using the curb-based segmentation algorithm.To evaluate the accuracy of the proposed method,the two-point cloud datasets of two typical test sites in an expressway environment consisting of flat and bumpy surfaces with a high slope were used.The proposed algorithm extracted the road surface successfully with high accuracy.There was an average recall of 99.5%,the precision was 96.3%,and the F1 score was 97.9%.From the extracted road surface,a framework for the estimation of road roughness was proposed.Good agreement was achieved when comparing the results of the road roughness map with the visual image,indicating the feasibility and effectiveness of the proposed framework.
基金supported by the Fundamental Research Funds for the Central Universities of China (Grant No.2013SCU11006)the Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying,Mapping and Geoinformation of China (Grant No.DM2014SC02)the Key Laboratory of Geospecial Information Technology,Ministry of Land and Resources of China (Grant No.KLGSIT201504)
文摘Using conventional Mean Shift Algorithm to segment high spatial resolution Remote sensing images of mountainous areas usually leads to an unsatisfactory result, due to its rich texture information. In this paper, we propose an improved Mean Shift Algorithm in consideration of the characteristics of these images. First, images were classified into several homogeneous color regions and texture regions by conducting variance detection on the color space. Next, each homogeneous color region was directly segmented to generate the preliminary results by applying the Mean Shift Algorithm. For each texture region, we conduct a high-dimensional feature space by extracting information such as color, texture and shape comprehensively, and work out a proper bandwidth according to the normalized distribution density. Then the bandwidth variable Mean Shift Algorithm was applied to obtain segmentation results by conducting the pattern classification in feature space. Last, the final results were obtained by merging these regions by means of the constructed cost functions and removing the oversegmented regions from the merged regions. It has been experimentally segmented on the high spatial resolution remote sensing images collected by Quickbird and Unmanned Aerial Vehicle(UAV). We put forward an approach to evaluate the segmentation results by using the segmentation matching index(SMI). This takes into consideration both the area and the spectrum. The experimental results suggest that the improved Mean Shift Algorithm outperforms the conventional one in terms of accuracy of segmentation.
基金Sponsored by Shanghai Leading Academic Discipline Project(Grant No T0603)the National Natural Science Foundation of China (Grant No60271033)
文摘A two-stage method for image segmentation based on edge and region information is proposed. Different deformation schemes are used at two stages for segmenting the object correctly in image plane. At the first stage, the contour of the model is divided into several segments hierarchically that deform respectively using affine transformation. After the contour is deformed to the approximate boundary of object, a fine match mechanism using statistical information of local region to redefine the external energy of the model is used to make the contour fit the object's boundary exactly. The algorithm is effective, as the hierarchical segmental deformation makes use of the globe and local information of the image, the affine transformation keeps the consistency of the model, and the reformative approaches of computing the internal energy and external energy are proposed to reduce the algorithm complexity. The adaptive method of defining the search area at the second stage makes the model converge quickly. The experimental results indicate that the proposed model is effective and robust to local minima and able to search for concave objects.
基金supported by PRIN-MIUR-Cofin 2006by University of Bologna"Funds for selected research topics"
文摘This paper introduces the use of partition of unity method for the development of a high order finite volume discretization scheme on unstructured grids for solving diffusion models based on partial differential equations.The unknown function and its gradient can be accurately reconstructed using high order optimal recovery based on radial basis functions.The methodology proposed is applied to the noise removal problem in functional surfaces and images.Numerical results demonstrate the effectiveness of the new numerical approach and provide experimental order of convergence.
文摘An improved approach for JSEG is presented for unsupervised segmentation of homogeneous regions in gray-scale images. Instead of intensity quantization, an automatic classification method based on scale space-based clustering is used for nonparametric clustering of image data set. Then EM algorithm with classification achieved by space-based classification scheme as initial data used to achieve Gaussian mixture modelling of image data set that is utilized for the calculation of soft J value. Original region growing algorithm is then used to segment the image based on the multiscale soft J-images. Experiments show that the new method can overcome the limitations of JSEG successfully.
