基于偏微分的图像放大算法在视频监控中的应用

视频监控是安全防范系统的重要组成部分,它是一种防范能力较强的综合系统。视频监控以其直观、准确、及时和信息内容丰富而广泛应用于许多场合。针对视频监控中画面回放的特殊场合和获得较好的视觉效果的需求,相对于传统放大算法难以获得高分辨率的优质图像的缺点,基于偏微分的图像放大算法,能较好地保持图像的边缘特征和细节信息,从而获得清晰的放大图像。     

浅谈高清视频监控

自近几年“高清”的概念在视频监控行业火热以来,越多来越多的厂商开始推出“高清”化的产品。透过市场上种类繁多的“高清产品”,我们发现,有很多产品对“高清”的指标理解不一,表现效果也是千差万别。那么,视频监控行业“高清”的概念究竟是什么？什么才是真正的“高清”？本文中,我们浅作探讨。     

网络化高清视频监控系统解析

从全高清的角度分析了准东二矿网络化高清视频监控系统从前端信号采集、传输、解码、存储到显示、应用的全过程,重点分析了高清视频流对网络资源的占用以及控制方法。高清视频监控系统为直观调度指挥生产、辅助矿井综合自动化系统、保障生产安全奠定了基础。     

煤矿视频监控图像的改进非局部均值滤波算法

针对井下光线较差、粉尘大而导致视频监控图像清晰度不佳的问题,提出了一种改进非局部均值滤波算法。首先采用Log边缘检测算子对图像进行边缘提取,获得边缘和非边缘图像;然后分别从相似图像块获取方法以及权重值计算方法2个方面对非局部均值滤波算法进行改进,用于去除非边缘图像中的噪声点;最后将边缘图像与滤波后的非边缘图像进行融合。分别采用该算法与中值滤波算法、均值滤波算法、非局部均值滤波算法对现场采集的图像进行测试,结果表明该算法的图像处理效果明显优于其他算法。     

高清视频监控在校园安全防范中的应用

本文首先根据当前现状,分析了校园监控的意义,然后简单介绍了高清视频监控的发展现状,在此基础上,本文从需求分析、系统结构、系统功能等方面详细阐述了校园高清视频监控安防系统的技术方案,最后文章概括总结了系统的特点。     

基于监控视频图像的车辆测速

基于监控视频图像的车辆测速方法(视频测速)的工作原理,提出了一种视频测速的实现思路,指出了在间隔已知时间的视频帧图像中找到对应块是实现视频测速的关键和难点。对车灯的特征进行了分析,根据车灯区域的特征,提出了采用灰度差水平叠加投影的方法,构造可以代表其鼓形区域的函数,以其作为定位车灯带的判别函数,并根据车灯的特点进行候选块筛选的一种简单快速的车灯区域定位方法,可在平均13ms内准确定位到车灯区域,从而为这一高速条件下视频测速的关键难点提出了一种切实可行的解决办法。     

图像监控系统浅议——公安视频图像监控联网平台

国内公安视频监控系统从最开始的单点监视到现在全城，全国范围的联网视频监控，监控系统已经从规模到功能上都发生了巨大的变化。本文结合目前的联网平台技术发展方向，深度剖析了视频图像监控联网所面临的问题。[编者按]     

基于DM368的高清视频监控系统设计与实现

DM368作为高清数字摄像机智能图像分析处理器,集成了高性能元件,可以组建嵌入式视频监控系统,且接口丰富、使用便捷。因此,本文以DM368为基础阐述了高清视频监控系统的设计方法,并进一步分析了高清视频监控系统的实现路径。     

公安指挥中心中的集群多级视频图像监控系统

介绍了公安指挥中心中集群多级视频图像监空系统的基本结构和组网形式,并有该系统的主CPU软件进行了详细阐述。     

Procedures for correcting high resolution airborne video imagery

Airborne videography is a useful way of producing very high resolution remotely-sensed data. Before video data can be used for digital analysis, individual frames must be correcled for geometric distortion and brightness variation. The most obvious geometric distortion occurs because the odd and even fields in a video frame are collected sequentially and can be displaced relative to each other by aircraft roll and forward motion. We describe a procedure for correcting this distortion based on the cross-correla lion between individual fields at different spatial lags. Brightness variation across frames occurs because of dilTerences in viewing geometry, bi-directional reflectance variation and atmospheric scattering. We describe a method for removing much of this variation by calculating scattering angle across the image with an optional adjustment for sensor plane tilt. Relations between scattering angle and scene brightness statistics may then be calculated from sequences of images collected along a given transect. These relations can be used 10 normalise brightness. Tests of normalisation procedures based on mean, standard deviation, and median brightness show that the median gives the best results. This approach also produces better results than commonly-used band ratioing procedures.     

Road network extraction from high resolution satellite images

In this paper, an approach of roads network extraction from high resolution satellite images is presented. First, the approach extracts road surface from satellite image using one-class support vector machine(SVM). Second, the road topology is built from the road surface. The last output of the approach is a series of road segments which is represented by a sequence of points as well as the topological relations among them. The approach includes four steps. In the first step one-class support vector machine is used for classifying pixel of the satellite images to road class or non-road class. In the second step filling holes and connecting gaps for the SVM's classification result is applied through mathematical morphology close operation. In the third step the road segment is extracted by a series of operations which include skeletonization, thin, branch pruning and road segmentation. In the last step a geometrical adjustment process is applied through analyzing the road segment curvature. The experiment results demonstrate its robustness and viability on extracting road network from high resolution satellite images.     

Invariant models for ground control points in high resolution images

This paper addresses the problem of automatic identification of ground control points (GCPs) in high resolution satellite digital images. The method is based on characteristics of GCPs and their neighbourhood, invariant with respect to rotation and contrast change. The characterization algorithm, which leads to a feature space with a fixed number of dimensions a critical advantage of the method is based on the autocorrelation function of an azimuthal projection around the GCP. Such features are first extracted from a set of reference images and stored in a database, together with the GCP geographical co-ordinates, and used in Euclidean minimum distance classifier, looking for matches with pixels of the image being geo-referenced. Practical issues lead to the necessity to filter out potential wrong identifications, through a consistency filter based on geometrical patterns made of ambiguous GCPs. The performance of the method is studied, demonstrating its insensitivity with respect to rotation and variations of contrast, its discrimination power, and its sensitivity to determine the absolute position of a GCP to within a fraction of pixel.     

Moment-based feature extraction from high spatial resolution hyperspectral images

In this article, we propose a method for extracting spatio-spectral features from high spatial resolution hyperspectral (HS) images. The method is based on extracting two-dimensional moments from neighbourhoods of pixels. Three different types of moments are considered: geometric, complex Zernike and Legendre. Moments of a given type are extracted from a few principal components (PC) of HS data, and are stacked on the original HS data to form a joint spatio-spectral feature space. These features are classified using a support vector machine (SVM) classifier. The influence of the moments orders and the size of the neighbourhood window on the quality of the extracted features are analysed. A few experiments are conducted on two widely used HS data sets, Pavia University and Salinas. The results demonstrate high capabilities of the proposed method in comparison with some state-of-the-art spatio-spectral HS classification methods.     

基于FPGA的四通道视频缩放引擎的研究及设计

设计了一种可实现4路视频信号缩放和帧率转换的电路架构.视频信号依次经过缩小模块、帧率转换模块以及放大模块,有效地减少了帧率转换对存储器带宽的需求.帧率变换模块采用输入和输出自适应调整的算法,同时在缩小模块采用加权均值算法,而放大模块则采用四点双三次插值算法.在满足视频放大质量要求的基础上,避免了采用过于复杂算法而消耗过...     

高分辨率SAR图像线状目标提取方法研究

本文针对高分辨率SAR影像进行城区河流、道路提取的算法研究,提出将纹理、面状和空间区域三种特征结合的方法。首先基于多尺度分析技术,结合面向对象的分割算法提取初始目标,之后使用灰度共生矩阵对目标及周边背景纹理进行差异对比分析去除误目标。与传统边缘检测方法相比,本文方法充分利用高分辨率SAR影像带来的纹理细节信息,并综合考虑目标的多种特征因素,增强了目标提取的可靠性,实用性强。     

Inertia based filtering of high resolution images using a GPU cluster

The scheme of inertia based anisotropic diffusion is a very powerful noise reducing and structure preserving image processing operator. This paper presents an implementation of this time consuming filter process on a cluster of Nvidia Tesla high performance computing processors, which can be applied to very large amounts of data in only a few minutes. Applying the inertia based diffusion filter to high resolution image stacks of neuron cells provides fully automatic geometric reconstructions of these images on a scale of <1μm. Such a high throughput and automatic image processing tool has great impact on various research areas, in particular the fast growing field of computational neuroscience, where one encounters increasing amount of microscopy data that needs to be processed.     

Accurate video text detection through classification of low and high contrast images

Detection of both scene text and graphic text in video images is gaining popularity in the area of information retrieval for efficient indexing and understanding the video. In this paper, we explore a new idea of classifying low contrast and high contrast video images in order to detect accurate boundary of the text lines in video images. In this work, high contrast refers to sharpness while low contrast refers to dim intensity values in the video images. The method introduces heuristic rules based on combination of filters and edge analysis for the classification purpose. The heuristic rules are derived based on the fact that the number of Sobel edge components is more than the number of Canny edge components in the case of high contrast video images, and vice versa for low contrast video images. In order to demonstrate the use of this classification on video text detection, we implement a method based on Sobel edges and texture features for detecting text in video images. Experiments are conducted using video images containing both graphic text and scene text with different fonts, sizes, languages, backgrounds. The results show that the proposed method outperforms existing methods in terms of detection rate, false alarm rate, misdetection rate and inaccurate boundary rate.     

数字视频监控系统中预录像的实现

以数字视频监控系统中的事件触发录像为背景,说明预录像的设计原理以及在事件触发录像中如何使用所设计的预录像方法实现数字视频监控系统中的事件触发录像功能,并给出了关键的C 程序实现代码。     

Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring

Mountain pine beetle red attack damage has been successfully detected and mapped using single-date high spatial resolution (< 4 m) satellite multi-spectral data. Forest managers; however, need to monitor locations for changes in beetle populations over time. Specifically, counts of individual trees attacked in successive years provide an indication of beetle population growth and dynamics. Surveys are typically used to estimate the ratio of green (current) attack trees to red (previous) attack trees or G:R. In this study, we estimate average stand-level G:R using a time-series of QuickBird multi-spectral and panchromatic satellite data, combined with field data for three forested stands near Merritt, British Columbia, Canada. Using a ratio of QuickBird red to green wavelengths (Red-Green Index or RGI), the change in RGI (ΔRGI) in successive image pairs is used to estimate red attack damage in 2004, 2005, and 2006, with true positive accuracies ranging from 89 to 93%. To overcome issues associated with differing viewing geometry and illumination angles that impair tracking of individual trees through time, segments are generated from the QuickBird multi-spectral data to identify small groups of trees. These segments then serve as the vehicle for monitoring changes in red attack damage over time. A local maxima filter is applied to the panchromatic data to estimate stem counts, thereby allowing an indication of the total stand population at risk of attack. By combining the red attack damage estimates with the local maxima stem counts, predictions are made of the number of attacked trees in a given year. Backcasting the current year's red attack damaged trees as the previous year's green attack facilitates the estimation of an average stand G:R. In this study area, these retrospective G:R values closely match those generated from field surveys. The results of this study indicate that a monitoring program using a time series of high spatial resolution remotely sensed data (multi-spectral and panchromatic) over select sample locations, could be used to estimate G:R over large areas, facilitating landscape level management strategies and/or providing a mechanism for assessing the efficacy of previously implemented strategies.