With the expansion of satellite constellation,routing techniques for small-scale satellite networks have problems in routing overhead and forwarding efficiency.This paper proposes a vector segment routing method for l...With the expansion of satellite constellation,routing techniques for small-scale satellite networks have problems in routing overhead and forwarding efficiency.This paper proposes a vector segment routing method for large-scale multi layer satellite networks.A vector forwarding path is built based on the location between the source and the destination.Data packets are forwarded along this vector path,shielding the influence of satellite motion on routing forwarding.Then,a dynamic route maintenance strategy is suggested.In a multi layer satellite network,the low-orbit satellites are in charge of computing the routing tables for one area,and the routing paths are dynamically adjusted in the area in accordance with the network.The medium-orbit satellites maintain the connectivity of vector paths in multiple segmented areas.The forwarding mode based on the source and destination location improves the forwarding efficiency,and the segmented route maintenance mode decreases the routing overhead.The simulation results indicate that vector segment routing has significant performance advantages in end-to-end delay,packet loss rate,and throughput in a multi layer satellite network.We also simulate the impact of routing table update mechanism on network performance and overhead and give the performance of segmented vector routing in multi layer low-orbit satellite networks.展开更多
Minutiae-based fingerprint matching is the most commonly used in an automatic fingerprint identification system. In this paper, we propose a minutia matching method based on line segment vector. This method uses all t...Minutiae-based fingerprint matching is the most commonly used in an automatic fingerprint identification system. In this paper, we propose a minutia matching method based on line segment vector. This method uses all the detected minutiae （the ridge ending and the ridge bifurcation） in a fingerprint image to create a set of new vectors （line segment vector）. Using these vectors, we can determine a truer reference point more efficiently. In addition, this new minutiae vector can also increase the accuracy of the minutiae matching. By experiment on the public domain collections of fingerprint images fvc2004 DID set A and DB4 set A, the result shows that our algorithm can obtain an improved verification performance.展开更多
In this paper an efficient compressed domain moving object segmentation algorithm is proposed, in which the motion vector （MV） field parsed from the compressed video is the only cue used for moving object segmentati...In this paper an efficient compressed domain moving object segmentation algorithm is proposed, in which the motion vector （MV） field parsed from the compressed video is the only cue used for moving object segmentation. First the MV field is temporally and spatially normalized, and then accumulated by an iterative backward projection to enhance salient motions and alleviate noisy MVs. The accumulated MV field is then segmented into motion-homogenous regions using a modified statistical region growing approach. Finally, moving object regions are extracted in turn based on minimization of the joint prediction error using the estimated motion models of two region sets containing the candidate object region and other remaining regions, respectively. Experimental results on several H.264 compressed video sequences demonstrate good segmentation performance.展开更多
Image segmentation is a necessary step in image analysis. Support vector machine （SVM） approach is proposed to segment images and its segmentation performance is evaluated. Experimental results show that： the effec...Image segmentation is a necessary step in image analysis. Support vector machine （SVM） approach is proposed to segment images and its segmentation performance is evaluated. Experimental results show that： the effects of kernel function and model parameters on the segmentation performance are significant; SVM approach is less sensitive to noise in image segmentation; The segmentation performance of SVM approach is better than that of back-propagation multi-layer perceptron （BP-MLP） approach and fuzzy c-means （FCM） approach.展开更多
In this paper, we propose a fast centerline extraction method to be used for gradient and direction vector flow of active contours. The gradient and direction vector flow is a recently reported active contour model ca...In this paper, we propose a fast centerline extraction method to be used for gradient and direction vector flow of active contours. The gradient and direction vector flow is a recently reported active contour model capable of significantly improving the image segmentation performance especially for complex object shape, by seamlessly integrating gradient vector flow and prior directional information. Since the prior directional information is provided by manual line drawing, it can be inconvenient for inexperienced users who might have difficulty in finding the best place to draw the directional lines to achieve the best segmentation performance. This paper describes a method to overcome this problem by automatically extracting centerlines to guide the users for providing the right directional information. Experimental results on synthetic and real images demonstrate the feasibility of the proposed method.展开更多
在机载锂电池失效识别等样本不平衡的应用场景中,支持向量机(support vector machine,SVM)算法存在分离超平面偏移的问题,为此,提出分段惩罚参数支持向量机(segmented penalty parameters support vector machine,SPP-SVM)算法.该算法...在机载锂电池失效识别等样本不平衡的应用场景中,支持向量机(support vector machine,SVM)算法存在分离超平面偏移的问题,为此,提出分段惩罚参数支持向量机(segmented penalty parameters support vector machine,SPP-SVM)算法.该算法在训练过程中对样本进行分段,并根据各段内样本的识别误差自动调整惩罚参数,从而抑制超平面偏移;基于容量增量分析和灰色关联分析等方法提取并筛选特征,进而基于SPP-SVM算法建立锂电池失效识别模型;以NASA锂电池数据集和加州大学欧文分校(University of California Irvine,UCI)数据集为对象,开展对比实验.研究结果表明:与结合寻优算法的SVM相比,SPP-SVM算法识别性能更好,在不平衡程度较大的锂电池数据上,查准率和查全率的调和平均数(F1值)提升11.7%;在锂电池数据集和UCI数据集上的训练耗时缩短,减少幅度超过10倍;证明在样本不平衡情况下,使用SPP-SVM算法能够有效抑制分离超平面偏移,提升识别效果.展开更多
为了解决直推式支持向量机(transductive support vector machines,TSVM)在样本选择自动化程度低和特征学习充分性不足的问题,提出了一种融合深度空间特征与传统影像对象特征的TSVM自动高分遥感影像变化检测方法。首先,采用基于分形网...为了解决直推式支持向量机(transductive support vector machines,TSVM)在样本选择自动化程度低和特征学习充分性不足的问题,提出了一种融合深度空间特征与传统影像对象特征的TSVM自动高分遥感影像变化检测方法。首先,采用基于分形网络演化算法的叠置分割获取多时相高分遥感影像的影像对象,通过卷积神经网络提取遥感影像的深度空间特征,并与灰度、指数和纹理等传统影像对象特征联合构建特征空间;然后,利用卡方变换计算多维特征的加权特征差异度,采用最大期望算法和贝叶斯最小错误判别规则得到二值分割结果,依据变化概率自动将分割结果中准确率较高的部分标记为训练样本;最后,采用标记训练样本获得TSVM的多维特征空间二值分割超平面,进而完成自动变化检测。选择武汉市的两组高分数据集作为实验数据。实验结果表明,该方法能够实现样本自动选择,并且通过融合深度空间特征可以有效提高特征学习的充分性,平均准确率达到了88.84%,平均漏检率较仅利用传统影像对象特征的TSVM法降低了3.29个百分点,在定性和定量的变化检测有效性评价中均得到了提高。展开更多
基金supported in part by the National Key R&D Program of China under Grant 2020YFB1806100in part by the Natural Science Foundation of China under Grant U19B2025 and Grant 62001347+1 种基金in part by the Key Research and Development Program of Shaanxi under Grants 2022ZDLGY05-02 and 2021KWZ-05in part by the Fundamental Research Funds for the Central Universities under Grant QTZX22161
文摘With the expansion of satellite constellation,routing techniques for small-scale satellite networks have problems in routing overhead and forwarding efficiency.This paper proposes a vector segment routing method for large-scale multi layer satellite networks.A vector forwarding path is built based on the location between the source and the destination.Data packets are forwarded along this vector path,shielding the influence of satellite motion on routing forwarding.Then,a dynamic route maintenance strategy is suggested.In a multi layer satellite network,the low-orbit satellites are in charge of computing the routing tables for one area,and the routing paths are dynamically adjusted in the area in accordance with the network.The medium-orbit satellites maintain the connectivity of vector paths in multiple segmented areas.The forwarding mode based on the source and destination location improves the forwarding efficiency,and the segmented route maintenance mode decreases the routing overhead.The simulation results indicate that vector segment routing has significant performance advantages in end-to-end delay,packet loss rate,and throughput in a multi layer satellite network.We also simulate the impact of routing table update mechanism on network performance and overhead and give the performance of segmented vector routing in multi layer low-orbit satellite networks.
文摘Minutiae-based fingerprint matching is the most commonly used in an automatic fingerprint identification system. In this paper, we propose a minutia matching method based on line segment vector. This method uses all the detected minutiae （the ridge ending and the ridge bifurcation） in a fingerprint image to create a set of new vectors （line segment vector）. Using these vectors, we can determine a truer reference point more efficiently. In addition, this new minutiae vector can also increase the accuracy of the minutiae matching. By experiment on the public domain collections of fingerprint images fvc2004 DID set A and DB4 set A, the result shows that our algorithm can obtain an improved verification performance.
基金Project supported by the National Natural Science Foundation of China （Grant No.60572127）, the Development Foundation of Shanghai Municipal Commission of Education （Grant No.05AZ43）, and the Shanghai Leading Academic Discipline Project （Grant No.T0102）
文摘In this paper an efficient compressed domain moving object segmentation algorithm is proposed, in which the motion vector （MV） field parsed from the compressed video is the only cue used for moving object segmentation. First the MV field is temporally and spatially normalized, and then accumulated by an iterative backward projection to enhance salient motions and alleviate noisy MVs. The accumulated MV field is then segmented into motion-homogenous regions using a modified statistical region growing approach. Finally, moving object regions are extracted in turn based on minimization of the joint prediction error using the estimated motion models of two region sets containing the candidate object region and other remaining regions, respectively. Experimental results on several H.264 compressed video sequences demonstrate good segmentation performance.
基金Supported by the National Natural Science Foundation of China (No. 60475024)
文摘Image segmentation is a necessary step in image analysis. Support vector machine （SVM） approach is proposed to segment images and its segmentation performance is evaluated. Experimental results show that： the effects of kernel function and model parameters on the segmentation performance are significant; SVM approach is less sensitive to noise in image segmentation; The segmentation performance of SVM approach is better than that of back-propagation multi-layer perceptron （BP-MLP） approach and fuzzy c-means （FCM） approach.
文摘In this paper, we propose a fast centerline extraction method to be used for gradient and direction vector flow of active contours. The gradient and direction vector flow is a recently reported active contour model capable of significantly improving the image segmentation performance especially for complex object shape, by seamlessly integrating gradient vector flow and prior directional information. Since the prior directional information is provided by manual line drawing, it can be inconvenient for inexperienced users who might have difficulty in finding the best place to draw the directional lines to achieve the best segmentation performance. This paper describes a method to overcome this problem by automatically extracting centerlines to guide the users for providing the right directional information. Experimental results on synthetic and real images demonstrate the feasibility of the proposed method.
文摘在机载锂电池失效识别等样本不平衡的应用场景中,支持向量机(support vector machine,SVM)算法存在分离超平面偏移的问题,为此,提出分段惩罚参数支持向量机(segmented penalty parameters support vector machine,SPP-SVM)算法.该算法在训练过程中对样本进行分段,并根据各段内样本的识别误差自动调整惩罚参数,从而抑制超平面偏移;基于容量增量分析和灰色关联分析等方法提取并筛选特征,进而基于SPP-SVM算法建立锂电池失效识别模型;以NASA锂电池数据集和加州大学欧文分校(University of California Irvine,UCI)数据集为对象,开展对比实验.研究结果表明:与结合寻优算法的SVM相比,SPP-SVM算法识别性能更好,在不平衡程度较大的锂电池数据上,查准率和查全率的调和平均数(F1值)提升11.7%;在锂电池数据集和UCI数据集上的训练耗时缩短,减少幅度超过10倍;证明在样本不平衡情况下,使用SPP-SVM算法能够有效抑制分离超平面偏移,提升识别效果.
文摘为了解决直推式支持向量机(transductive support vector machines,TSVM)在样本选择自动化程度低和特征学习充分性不足的问题,提出了一种融合深度空间特征与传统影像对象特征的TSVM自动高分遥感影像变化检测方法。首先,采用基于分形网络演化算法的叠置分割获取多时相高分遥感影像的影像对象,通过卷积神经网络提取遥感影像的深度空间特征,并与灰度、指数和纹理等传统影像对象特征联合构建特征空间;然后,利用卡方变换计算多维特征的加权特征差异度,采用最大期望算法和贝叶斯最小错误判别规则得到二值分割结果,依据变化概率自动将分割结果中准确率较高的部分标记为训练样本;最后,采用标记训练样本获得TSVM的多维特征空间二值分割超平面,进而完成自动变化检测。选择武汉市的两组高分数据集作为实验数据。实验结果表明,该方法能够实现样本自动选择,并且通过融合深度空间特征可以有效提高特征学习的充分性,平均准确率达到了88.84%,平均漏检率较仅利用传统影像对象特征的TSVM法降低了3.29个百分点,在定性和定量的变化检测有效性评价中均得到了提高。
