Projection based statistical feature extraction with multispectral images and its applications on the Yellow River mainstream line detection

Mainstream line is significant for the Yellow River situation forecasting and flood control. An effective statistical feature extraction method is proposed in this paper. In this method, a between-class scattering matrix based projection algorithm is performed to maximize between-class differences, obtaining effective component for classification; then high-order statistics are utilized as the features to describe the mainstream line in the principal component obtained. Experiments are performed to verify the applicability of the algorithm. The results both on synthesized and real scenes indicate that this approach could extract the mainstream line of the Yellow River automatically, and has a high precision in mainstream line detection. Communication author: Zhang Haichao, born in 1986, male, Ph.D. candidate.     

No-reference quality assessment of dynamic sports videos based on a spatiotemporal motion model

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.     

高阶周期平稳连续随机信号离散化问题的研究

本文给出了高阶周期平稳随机过程连续时间信号与离散时间信号循环统计量之间的关系,该关系式较之文献［２］中的表达式更为简单明确;在讨论了连续信号循环频率的支撑区及循环矩支撑区的基础之上,给出了连续信号在时间离散化时不发生混迭的条件;从而由离散信号的循环统计量可以得到连续信号的循环统计量。     

定时截尾Weibull分布的双参数的矩估计

讨论了在定时截尾情形下,将Weibull分布转化成指数分布数据或均匀分布,利用平均剩余寿命构造样本矩．从而得出参数的矩估计。并通过大量的Monte-Garlo数值模拟试验证实了所给方法的可行性。     

Image annotation using high order statistics in non-Euclidean spaces

Automatic image annotation is a promising way to achieve more effective image retrieval and image analysis by using keywords associated to the image content. Due to the semantic gap between low-level visual features and high-level semantic concepts of an image, however, the performances of many existing algorithms are not so satisfactory. In this paper, a novel image classification scheme, named high order statistics based maximum a posterior (HOS-MAP), is proposed to deal with the issue of image annotation. To bridge the gap between human judgment and machine intelligence, the proposed scheme first constructs a dissimilarity representation for each image in a non-Euclidean space; then, the information of dissimilarity diffusion distribution for each image is achieved with respect to the high-order statistics of a triplet of nearest neighbor images; finally, a maximum a posteriori algorithm with the information of Gaussian Mixture Model and dissimilarity diffusion distribution is adopted to estimate the relevance between each annotation and an input un-annotated image. Experimental results on a general-purpose image database demonstrate the effectiveness and efficiency of the proposed automatic image annotation scheme.     

电离层斜向传播模式的智能识别

采用数值统计与数字图像处理中形态学算子相结合的方法获得了高质量斜向探测电离图.结合观测描迹统计特性,利用改进的初始局部最大值和约束条件外推方法,实现了斜向探测电离层传播模式的智能识别,利用该方法可以自动提取常规电离图各传播模式对应的频率、群时延及信号能量等信息.     

传送网规模统计与评估指标的研究

随着当前电信网络规模的迅速发展,传送网的规模越来越大,为了对传送网资源进行有效的管理,从宏观上把握传送网的建设规模,文章从统计和评估的角度对传送网的各种指标进行了分析研究,首次提出了简单而归一化的统计评估办法,对于传送网资源管理和规划具有重要的参考意义。     

大规模接入汇聚路由器基于用户的流量统计设计与实现

大规模汇聚接入路由器基于用户的流量统计需要存储大量的统计数据,传统的方法用D触发器进行存储,往往需要消耗大量的逻辑单元和布线资源.文中给出一种新的存储统计数据的设计方案,它有效利用FPGA内部的BlockRAM资源,从而节省了大量的逻辑单元和布线资源.该方案良好的可扩展性保证了随着用户数的增加,无需重新设计,仅仅增加BlockRam的大小即可完成在大规模网络中的基于用户的流量统计.     

一种有效的脉内特征分析算法

信号脉内特征分析技术对密集和复杂电磁环境中辐射源的分选和识别有着重要意义。从工程应用的角度出发,提出一种有效的脉内特征分析算法,将多阶相位差分计算得到的瞬时频率特征作为区分调频和调相信号的依据:通过逆序相乘法和瞬时频率直方图统计识别出线性调频和频率编码信号。通过倍频法进一步区分二相编码和四相编码信号,仿真试验表明:该算法在较低信噪比下对常见脉内调制信号具有较好的性能,运算量小,便于硬件实现。     

一种基于多尺度卷积神经网络和分类统计的图像去雾霾方法

传统的去雾霾方法会导致天空、白云和明亮区域内的颜色失真.为了解决以上问题,提出了一种基于多尺度卷积神经网络和分类统计的去除图像雾霾的方法.首先用多尺度卷积神经网络估计图像的透射率,其次对所估计的透射率进行分类统计以确定在暗通道内天空、白云和明亮区域的像素值,最后通过低通高斯滤波器平滑图像场景的辐射度,得到恢复的无雾霾图像.实验结果表明,采用提出的方法对图像去雾霾后明亮区域内的颜色不会失真,且保留了图像的自然外观,对合成图像和真实图像均有较好的去雾霾效果.