多域分级管理的网络视频监控指挥平台的设计与实现

本文重点介绍了一种基于多域分级管理模式的网络视频监控指挥平台该平台采用域的管理概念,将物理链路不通的域之间或彼此有层级从属关系的域之间以分级管理模式达到数据同步、相互通信,使独立的域之间以相互信任的管理模式实现信息互通,从而将并构网络之间、不同单位之间的资源共享,协同指挥部署,达到充分利用信息资源、避免重复建设、提高监控指挥效率的目的本文从系统构架、系统软件平台、系统功能三个方面详细介绍了这一多域分级管理的网络视频监控指挥平台     

基于无人机图像的输电线自动提取技术

对输电线路的检测能够确保电网的安全运行,利用无人机图像实现输电线准确、快速的提取是实现电力巡检的前提.本文提出了一种基于无人机图像的输电线自动提取方法,能够从复杂的背景图像中完成输电线的有效识别.首先,采用Ratio算子提取出电力线像素,并结合Hough变换对直线段进行检测;然后,根据输电线路几何特征设计了完整输电线合...     

一种基于双滤波的空间图像色域映射算法

针对逐点色域映射方法在图像色域映射时出现的细节损失较大的问题,本文提出了一种新的空间图像色域映射方法。新方法采用双滤波技术将输入图像分解为对应边缘轮廓信息的基础层图像和对应纹理细节信息的细节层图像,先对基础层图像进行彩度优先的逐点色域裁剪,然后将细节层信息补偿给色域裁剪后的图像。最后对细节补偿后的图像进行亮度优先的色域裁剪,从而得到最终的映射图像。本文还分析了双滤波参数设置对色域映射结果和光晕的影响,得出了合理的双滤波参数设置。通过心理物理学实验可以验证：新方法的性能与经典的细节补偿类映射方法相当。另外,新方法在色域映射过程中还能更加有效地抑制光晕的产生。     

基于平滑性测度的直方图自适应模糊增强图像分割

本文提出了一种新的基于平滑性测度的直方图自适应模糊增强图像分割方法。该方法通过定义图像的平滑性测度,采用模糊增强技术对图像的灰度直方图进行增强,然后在增强的直方图上,利用自适应多阈值分割方法进行图像分割。实验表明,该方法对强噪声图像具有良好的分割效果。     

基于图像质量评价的显示器色域映射

针对显示器图像颜色复现问题,提出了一种基于图像质量评价和图像分割-融合策略的色域映射算法。详细分析了各类色域映射算法的特点并选取4种算法作为基本色域映射算法。提出了一种色域映射图像质量评价算法,并选用SLIC超像素算法对源图像分割。使用基本色域映射算法对源图像进行映射处理,并计算每个分块内各映射图像与源图像的相似度。根据每个图像分块的相似度,对基本色域映射图像进行选择综合处理并融合成最终图像。最后以LED显示屏和LCD显示器为例,对本文提出的算法和其他基本色域映射算法进行主观评价对比实验。实验结果证明本文算法在图像颜色保真效果上要明显优于其他算法,但是在计算速度上仍有待优化。     

基于帧映射算法的无线实时视频监控系统

针对无线实时视频监控系统的特点，提出了一种有效节省网络带宽的算法——帧映射算法，并用此算法实现了一个无线实时视频监控系统的原型系统。     

基于域鉴别网络和域自适应的行人重识别

针对行人重识别无监督跨域迁移问题,提出一种 基于域鉴别网络和域自适应的行人重识别算法。首先,使用改 进ResNet-50训练监督域鉴别网络模型,加入共享空间组件得到特征 不变属性,用于区分类间图像,并基 于对比损失和差异损失来提高模型的分类性能。其次,利用域自适应无监督迁移方法由源域 数据集导出特 征不变属性,并应用到未标记的目标域数据集上。最后,匹配查询图像和共享空间中的图库 图像执行跨域 行人重识别。为验证算法有效性,在CUHK03、Market-1501和DukeMTMC-reID数据集上进行了实验,算法 在Rank-1准确度分别达到34.1%、38.1%和28.3%,在mAP分别达到34.2%、17. 1%和17.5%,最后还验证了 模型各个组件在训练阶段的必要性。结果表明本文算法在大规模数据集上的性能优于现有的 一些无监督行人重识别方法,甚至接近于某些传统监督学习方法的性能。     

DCT域统计特性的图像重采样检测

针对现存重采样检测算法仅能适用于无损图像检测的现状,提出了一种适用范围更广、鲁棒性更强的图像重采样检测方法。本算法克服了以往算法中下采样检测效果较差的局限并对幅度较小的插值操作也有较好的检测效果。借助DCT域AC系数首位有效数字的概率分布分别对RGB3个色彩通道进行统计,以3条概率曲线的拟合程度为依据对重采样操作进行检测。实验结果表明本方法对于常用的图像重采样方法都有较高的检测率。     

Multilevel segmentation of histopathological images using cooccurrence of tissue objects

This paper presents a new approach for unsupervised segmentation of histopathological tissue images. This approach has two main contributions. First, it introduces a new set of high-level texture features to represent the prior knowledge of spatial organization of the tissue components. These texture features are defined on the tissue components, which are approximately represented by tissue objects, and quantify the frequency of two component types being cooccurred in a particular spatial relationship. As they are defined on components, rather than on image pixels, these object cooccurrence features are expected to be less vulnerable to noise and variations that are typically observed at the pixel level of tissue images. Second, it proposes to obtain multiple segmentations by multilevel partitioning of a graph constructed on the tissue objects and combine them by an ensemble function. This multilevel graph partitioning algorithm introduces randomization in graph construction and refinements in its multilevel scheme to increase diversity of individual segmentations, and thus, improve the final result. The experiments on 200 colon tissue images reveal that the proposed approach--the object cooccurrence features together with the multilevel segmentation algorithm--is effective to obtain high-quality results. The experiments also show that it improves the segmentation results compared to the previous approaches.     

Tracking moving objects in television images

The estimation of the velocity of objects imaged by television cameras is useful in different areas of image processing.The problem is solved by means of a linear estimation algorithm and the effects of noise superimposed to the signal are analyzed. The structure of a real-time estimator is then presented. Experimental results show that a very fine accuracy is obtained. They encourage its application to image coding for redundancy reduction using movement compensation.     

Method of images segmentation by dynamic spectral characteristics

In considered method we use basic principles of biometrics and bioradiolocation for solution of the problem of images segmentation. Using dynamic spectral characteristics, obtained by means of wavelet spectrum we extract biometric indicator in form of signal of brightness pixels modification at the face skin part, specified by heartbeat. It is proposed to use quasi-matched wavelet filters for efficient selection of human heartbeat signal and it is shown the possibility of its frequency measurement practically at real-time mode. Obtained results can be used for many medical applications, security systems, object identification, etc.     

A novel transmission line characterisation based on measurement data reconfirmation

Due to inherent resonance effects and frequency-variant dielectric properties, it is very difficult to experimentally determine the stable and accurate circuit model parameters of thin film transmission line structures over a broad frequency band. In this article, a new, simple and straightforward frequency-variant transmission line circuit model parameter determination method is presented. Experimental test patterns for high-frequency transmission line characterisations are designed and fabricated using a package process. The S-parameters for the test patterns are measured using a vector network analyzer (VNA) from 100 MHz to 26.5 GHz. The parasitic effects due to contact pads are de-embedded. The frequency-variant complex permittivity and resonance-effect-free transmission line parameters (i.e., the propagation constant and characteristic impedance) are then determined in a broad frequency band.     

Adaptive river segmentation in SAR images

There is difficulty for distinguishing of river and shadow in Synthetic Aperture Radar (SAR) images. A method of river segmentation in SAR images based on wavelet energy and gradient is proposed in this paper. It mainly includes two algorithms: coarse segmentation and refined segmentation. Firstly, The river regions are coarsely segmented by the wavelet energy feature,and then refined segmented accurately by the gradient threshold which is got adaptively. The experimental results show the validity of the method, which provides a good foundation for targets detection above the river.     

Transmission line detection in aerial images: An instance segmentation approach based on multitask neural networks

Camera-based transmission line detection (TLD) is a fundamental and crucial task for automatically patrolling powerlines by aircraft. Motivated by instance segmentation, a TLD algorithm is proposed in this paper with a novel deep neural network, i.e., CableNet. The network structure is designed based on fully convolutional networks (FCNs) with two major improvements, considering the specific appearance characteristics of transmission lines. First, overlaying dilated convolutional layers and spatial convolutional layers are configured to better represent continuous long and thin cable shapes. Second, two branches of outputs are arranged to generate multidimensional feature maps for instance segmentation. Thus, cable pixels can be detected and assigned cable IDs simultaneously. Multiple experiments are conducted on aerial images, and the results show that the proposed algorithm obtains reliable detection performance and is superior to traditional TLD methods. Meanwhile, segmented pixels can be accurately identified as cable instances, contributing to line fitting for further applications.     

基于深度卷积神经网络的输电线路可见光图像目标检测

为了检测输电线路可见光图像中的塔材、玻璃绝缘子和复合绝缘子,本文采用了一种基于深度卷积神经网络的技术。通过有人直升机搭载高清相机拍摄19条不同的输电线路近600张图片,对图片中的背景、塔材、玻璃绝缘子和复合绝缘子目标进行人工标注及分块,采用数据扩展生成包含15万个样本的输电线路图像库。构造5层深度卷积神经网络,首先用Cifar-100数据集对网络进行预训练,然后用输电线路图像库进行网络调优。本文方法在检测真阳率为90%时,假阳率低于10%,明显优于传统方法,可用于输电线路可见光图像中的塔材、玻璃绝缘子和复合绝缘子检测,检测结果可用于诊断参考或进一步的目标状态分析。可对输电线路可见光图像中的塔材和绝缘子目标进行检测,并可扩展到其它类型目标的检测。     

一种基于区域显著性的红外图像目标分割方法

提出了一种基于区域显著性的红外图像目标分割方法,即首先在方差空间中提取显著性区域,然后根据图像复杂度对显著性区域进行筛选,最后采用阈值分割方法分割显著性区域,获取目标.算法具有较强的适用性和工程实用性.     

用全息双频光栅测量物体的振动

激光束入射至固定在振动物体上的全息双频光栅,其1级衍射光强度受到了振动的调制。本文介绍这种测振法的原理和一些实验结果,并对其应用作简要的讨论。     

Abnormality segmentation in brain images via distributed estimation

The aim of this paper is to introduce a novel semisupervised scheme for abnormality detection and segmentation in medical images. Semisupervised learning does not require pathology modeling and, thus, allows high degree of automation. In abnormality detection, a vector is characterized as anomalous if it does not comply with the probability distribution obtained from normal data. The estimation of the probability density function, however, is usually not feasible due to large data dimensionality. In order to overcome this challenge, we treat every image as a network of locally coherent image partitions (overlapping blocks). We formulate and maximize a strictly concave likelihood function estimating abnormality for each partition and fuse the local estimates into a globally optimal estimate that satisfies the consistency constraints, based on a distributed estimation algorithm. The likelihood function consists of a model and a data term and is formulated as a quadratic programming problem. The method is applied for automatically segmenting brain pathologies, such as simulated brain infarction and dysplasia, as well as real lesions in diabetes patients. The assessment of the method using receiver operating characteristic analysis demonstrates improvement in image segmentation over two-group analysis performed with Statistical Parametric Mapping (SPM).