Copy-move image forgery detection based on Gabor magnitude

With advancement of media editing software, even people who are not image processing experts can easily alter digital images. Various methods of digital image forgery exist, such as image splicing, copy-move forgery, and image retouching. The most common method of tampering with a digital image is copy-move forgery, in which a part of an image is duplicated and used to substitute another part of the same image at a different location. In this paper, we present an efficient and robust method to detect such artifacts. First, the tampered image is segmented into overlapping fixed-size blocks, and the Gabor filter is applied to each block. Thus, the image of Gabor magnitude represents each block. Secondly, statistical features are extracted from the histogram of orientated Gabor magnitude (HOGM) of overlapping blocks, and reduced features are generated for similarity measurement. Finally, feature vectors are sorted lexicographically, and duplicated image blocks are identified by finding similarity block pairs after suitable post-processing. To enhance the algorithm’s robustness, a few parameters are proposed for removing the wrong similar blocks. Experiment results demonstrate the ability of the proposed method to detect multiple examples of copy-move forgery and precisely locate the duplicated regions, even when dealing with images distorted by slight rotation and scaling, JPEG compression, blurring, and brightness adjustment.     

基于统计噪声水平分析的图像拼接检测

图像拼接是最常用的图像篡改操作之一,针对篡改图像噪声水平不一致性的现象,本文提出了一种基于统计噪声水平分析的图像拼接检测方法。首先,将检测图像分割成大小相同的非重叠图像块,然后,利用一种非参数估计算法来估计每个图像块的噪声值,并且采取聚类法对图像块的噪声值进行聚类,聚类结果分为可疑部分和非可疑部分两大类。最后,通过一个由粗到细的两阶段策略对篡改区域进行定位。哥伦比亚未压缩图像拼接检测评估图像库的实验结果表明,本文方法能够准确地估计图像块的噪声和定位出拼接区域,性能优于现有方法。     

分布式MIMO雷达的参数估计与检测联合算法

本文针对分布式MIMO雷达系统,在站间大间隔配置获得的空间分集增益的基础上,提出了一种目标位置估计与检测的联合算法。与以往距离门检测不同的是,这里通过所定义的目标假设框架下进行联合估计与检测。通过理论分析证明,本文所提出的位置估计与检测联合算法在检测性能上要优于距离门检测法,且漏检概率与信噪比SNR成反比。仿真实验也验证了算法的有效性。     

基于残留噪声相关性的视频篡改检测算法

提出了一种基于残留噪声相关性的视频篡改检测算法.该算法利用双树复小波域局部维纳滤波的方法获取视频每帧的残留噪声,相邻两残留噪声帧对应块做相关性运算,根据相关系数的统计特性建立累积分布函数模型,设定最佳阈值,定位篡改区域.所提出的提取残留噪声的方法能更好地保留图像细节,减少残留噪声中的场景污迹.实验结果验证了该算法检测篡改视频的准确率更高.     

基于肤色检测的快速五官定位算法

根据视频应用的特点，结合人脸的肤色和特征部位几何分布特征，提出了一种应用于视频序列人脸部位的五官定位算法。实验表明，该算法定位速度快，误检率低。     

The variant cycle-cover problem in fault detection and localization for mesh all-optical networks

With the soaring channel speed and density in all-optical networks (AONs), the risk of high data loss upon network faults increases quickly. To manage network faults efficiently, an m-cycle based fault detection and localization (MFDL) scheme has been introduced recently. This paper verifies the necessary and sufficient condition for achieving the complete fault localization (CFL) in MFDL, which is defined as the case that every single network fault can be located to a unique link. We model the m-cycle construction as a new mathematical problem: the variant version of the constrained cycle-cover problem (vCCCP) and explore its formal expression. The model includes the consideration of the cycle-length limit, cycle number, and wavelength cost, while also keeps the CFL achievable. A two-phase branch-and-bound (B&B) algorithm was developed for solving the vCCCP, which guarantees to find near-optimal solutions. This algorithm is then applied to four typical and four random network examples to validate and assess the performance. The results are analyzed and compared with some previously reported algorithms, in terms of fault localization degree, cycle number, wavelength overhead, and cost reduction. The performance evaluation and comparison reveal that the new model and algorithm could significantly reduce the MFDL cost, including both the cost of monitoring devices and reserved wavelengths.     

Secure localization with phantom node detection

In an adversarial environment, various kinds of attacks become possible if malicious nodes could claim fake locations that are different from their physical locations. In this paper, we propose a secure localization mechanism that detects existence of these nodes, termed as phantom nodes, without relying on any trusted entities, an approach significantly different from the existing ones. The proposed mechanism enjoys a set of nice features. First, it does not have any central point of attack. All nodes play the role of verifier, by generating local map, i.e. a view constructed based on ranging information from its neighbors. Second, this distributed and localized construction results in strong robustness against adversaries: even when the number of phantom nodes is greater than that of honest nodes, we can filter out most of the phantom nodes. Our analytical results as well as simulations under realistic noisy settings demonstrate that the proposed mechanism is effective in the presence of a large number of phantom nodes.     

Exposing photo manipulation with inconsistent perspective geometry

Manipulated digital image is got interesting in recent years. Digital images can be manipulated more easily with the aid of powerful image editing software. Forensic techniques for authenticating the integrity of digital images and exposing forgeries are urgently needed. A geometric-based forensic technique which exploits the principle of vanishing points is proposed. By means of edge detection and straight lines extraction, intersection points of the projected parallel lines are computed. The normalized mean value （NMV） and normalized standard deviation （NSD） of the distances between the intersection points are used as evidence for image forensics. The proposed method employs basic rules of linear perspective projection, and makes minimal assumption. The only requirement is that the parallel lines are contained in the image. Unlike other forensic techniques which are based on low-level statistics, this method is less sensitive to image operations that do not alter image content, such as image resampling, color manipulation, and lossy compression. This method is demonstrated with images from York Urban database. It shows that the proposed method has a definite advantage at separating authentic and forged images.     

基于聚类和特征检测的焊缝和钨针定位方法

钢管氩弧焊接过程中需要实时判断钨针针尖与钢管焊缝中心线是否对齐,以控制焊接质量。用肉眼判断误差较大,且效率较低。为此,提出一种基于聚类和特征检测的氩弧焊图像中焊缝中心线坐标和钨针针尖坐标位置计算方法。该方法利用幂次变换的方法增强图像对比度,用直觉模糊C均值聚类方法对图像进行二值化,用Canny算子边缘检测的方法提取图像特征,检测出钢管氩弧焊图像中的熔池、焊缝中心线以及钨针针尖位置。实验结果表明,该方法能够快速准确地定位焊缝中心线和钨针针尖位置。     

基于分形维数和FCM聚类的多时相SAR图像变化检测

提出了一种基于分形维数和FCM聚类的SAR图像无监督变化检测的算法。首先用非下采样Contourlet变换(NSCT),对两时相图像进行分解,然后求出其分形维数图,构造差异图,再由FCM聚类得到变化区域和非变化区域。并对计算分形维的滑动窗口大小的选择进行了研究。与现有的基于分形维数的图像变化检测算法进行对比,实验证实,本文算法不仅对斑点噪声不敏感,并且提高了变化检测的精确度。     

计算机图像处理技术在激光—荧光法肺癌定位中的应用

介绍了激光－荧光法早期肺癌诊断和定位技术，并提出一种与计算机图像处理技术结合的新方法，其结果改善诊断和定位的准确率，有效地克服当前激光诱导荧光早期肺癌诊断可能出现的假设阳性问题。     

计算机图像处理技术在激光－荧光法肺癌定位中的应用

介绍了激光－荧光法早期肺癌诊断和定位技术，并提出一种与计算机图像处理技术结合的新方法。其结果将改善诊断和定位的准确率，有效地克服当前激光诱导荧光早期肺癌诊断可能出现的假阳性问题。     

Source localization for multiple speech sources using low complexity non-parametric source separation and clustering

This article presents a new method for localization of multiple concurrent speech sources that relies on simultaneous blind signal separation and direction of arrival (DOA) estimation, as well as a method to solve the intersection point selection problem that arises when locating multiple speech sources using multiple sensor arrays. The proposed method is based on a low complexity non-parametric blind signal separation method, making is suitable for real-time applications on embedded platforms. On top of reduced complexity in comparison to a previously presented method, the DOA estimation accuracy is also improved. Evaluation of the performance is done with both real recording and simulations, and a real-time prototype of the proposed method has been implemented on a DSP platform to evaluate the computational and the memory complexities in a real application.     

图像显著性检测方法解析

图像显著性检测是一种通过对图像颜色、强度、方向等特征进行分析生成图像显著性图的技术。其生成的显著性图可以用于图像分割、图像压缩以及图像识别等图像处理领域,从而改善图像处理的性能。为了对图像显著性检测技术及其发展有一个全面深入的了解,使用文献研究法和比较研究法对其概念及方法进行了探究。针对几种具有代表性的图像显著性检测算法进行了简要的概述和分析,用流程图简明扼要地表示显著性检测算法的基本框架。研究结果显示,图像显著性检测技术的效率在不断提升,算法越来越多样化,在图像处理领域的应用越来越广泛,这些对于图像处理自动化具有重要意义。     

A novel system for robust lane detection and tracking

This paper presents a lane detection and tracking system based on a novel lane feature extraction approach and the Gaussian Sum Particle filter (GSPF). The proposed feature extraction approach is based on the fact that by zooming into the vanishing point of the lanes, the lane markings/boundaries will only move on the same straight lines they are on. Objects other than the lanes in the frame do not share this property and can be ignored during the model parameter estimation. This algorithm is able to iteratively refine various traditional feature maps and to operate with curved roads. The tracking part of the system is initialised by a deformable template matching algorithm. Three types of tracking algorithms are compared in our study: the original Sequential Importance Resampling (SIR) particle filter, the Gaussian Particle Filter (GPF) and the Gaussian Sum Particles Filter (GSPF). The GSPF achieves the best performance by integrating a novel likelihood function and an intuitive parameter selection process. Both the GSPF and GPF provide improved tracking performance and require less computational power than the SIR. It has also been found that the detection and tracking performance is enhanced significantly by incorporating the refined feature map.     

Data-driven design of robust fault detection system for wind turbines

In this paper, a robust data-driven fault detection approach is proposed with application to a wind turbine benchmark. The main challenges of the wind turbine fault detection lie in its nonlinearity, unknown disturbances as well as significant measurement noise. To overcome these difficulties, a data-driven fault detection scheme is proposed with robust residual generators directly constructed from available process data. A performance index and an optimization criterion are proposed to achieve the robustness of the residual signals related to the disturbances. For the residual evaluation, a proper evaluation approach as well as a suitable decision logic is given to make a correct final decision. The effectiveness of the proposed approach is finally illustrated by simulations on the wind turbine benchmark model.     

Impulsive noise detection by second-order differential image and noise removal using adaptive nearest neighbourhood filter

A novel impulsive noise detection method based on the principle that the difference between the noisy pixel and the nearest good pixel will be different from the difference between two nearby good pixels. This is achieved by constructing a second-order differential image. Three new noise removal methods are presented. Simulated results show that the proposed filter gives far better results than many existing filters and is comparable to the results obtained by JM filter based on Jarque-Bera test. Our noise detection method is computationally simpler.     

基于摄像头检测的防盗拍系统开发和算法研究

针对电影院中的非法盗拍现象,提出了对智能摄像头检测系统的改造和算法设计。硬件部分的人工光源模块提供定向光照,与之相配合的软件算法部分融合层级特征检测、直方图比较、主成分分析、K最近邻分析等多种方法,对摄像头镜头反射高光模式进行检测和识别。通过实验数据集的验证,系统对摄像头敏感度高,检测准确率高,具有理论研究价值和实际应用价值。系统硬件模块和算法模块可拓展且可移植于其他应用领域。     

Depth-based detection with region comparison features

Most object detection approaches proposed over the years rely on visual features that help to segregate objects from their backgrounds. For instance, segregation may be facilitated by depth features because they provide direct access to the third dimension. Such access enables accurate object-background segregation. Although they provide a rich source of information, depth images are sensitive to background noise. This paper addresses the issue of handling background noise for accurate foreground–background segregation. It presents and evaluates the Region Comparison (RC) features for fast and accurate body part detection. RC features are depth features inspired by the well-known Viola–Jones detector. Their performances are compared to the recently proposed Pixel Comparison (PC) features, which were designed for fast and accurate object detection from Kinect-generated depth images. The results of our evaluation reveal that RC features outperform PC features in detection accuracy and computational efficiency. From these results we may conclude that RC features are to be preferred over PC features to achieve accurate and fast object detection in noisy depth images.