基于报警时序信息挖掘的报警关联分析方法

面对现代流程工业监控系统报警泛滥问题,为了寻找报警根源以减少无效报警,并针对传统方法在面对大量的报警数据时计算效率低下的问题,提出了一种基于报警数据时序信息挖掘的报警关联分析方法.通过将报警时序信息进行区块化处理,将报警时间序列转换为报警时间的节点序列,然后将区块之间的匹配度作为报警关联度的评价标准,减少了关联分析的运算量;采用滑动窗口比对计算相关报警时间序列的时间关联信息;采用TE过程（Tennessee Eastman process）数据,验证了所提方法的有效性.     

The impact of false alarm mitigation on surface landmine detection in MWIR imagery

Surface landmine and minefield detection from airborne imagery is a difficult problem. As part of the minefield detection process, anomaly detection is performed to identify potential landmines in individual airborne images. Post-processing is performed on the initial landmines identified to reduce the number of false alarms, referred to as false alarm mitigation. In this research, a circular harmonics transform image processing approach (the CHT method) and a constant false alarm rate technique (the RX approach) are investigated for surface landmine detection and false alarm mitigation in medium wave infrared (MWIR) image data. The false alarm mitigation approach integrates the CHT and RX methods to identify candidate landmine locations with one technique at a given false alarm rate and applies the other technique to confirm landmine locations and eliminate potential false alarms. Individual detector and false alarm mitigation experimental results are presented for 31 daytime and 43 nighttime MWIR images containing 76 and 142 landmines, respectively. At a 0.9 desired probability of landmine detection, experimental results show that false alarm mitigation reduces the false alarm rate by as much as 84.3% and 13.7% for daytime and nighttime images, respectively, maintaining the probability of detection at 0.85 and 0.90, respectively.     

在基于上下文机场识别中减少虚警率的方法

主要针对基于上下文的机场识别算法存在较高虚警率的情况进行了改进，提出了一种新的减少虚警率的方法。此方法首先进行基于对比度特征的跑道识别，通过定义恰当的跑道对比度特征，采用两种判别准则对此对比度特征进行分类。在此基础上，进一步完善联络道的识别方法，使之能排除剩下虚警的情况。经实验表明，绝大部分的虚警都能在前面的跑道识别中排除，剩下的则能在联络道识别中排除。     

基于肤色和行为的色情视频检测

传统色情视频识别方法大多是色情图像识别方法的直接扩展,没有考虑到“行为”这一包含在色情视频中的关键信息。光流上下文直方图能描述运动物体的连续动作,基于此,提出了一种新的用于描述行为的特征——光流上下文直方图（OFCH）,并采用主成分分析（PCA）进行特征降维,得到的PCA-OFCH特征用于训练敏感行为识别器;同时采用基于直方图技术的贝叶斯肤色预测模型对视频中是否含有足够的肤色信息进行判断,以降低对正常行为的误报率。实验结果表明,提出的基于PCA-OFCH特征结合肤色检测能有效地对色情视频和正常视频进行鉴别,为色情视频识别提供了新的思路。     

License plate detection based on multistage information fusion

Adaboost detector has been successfully used in object detection. In this paper, we propose a new License Plate (LP) detection technique based on multistage information fusion, which is adopted to reduce high false alarm rate in the conventional Adaboost detector. The proposed multistage information fusion system is composed of an enhanced Adaboost detector, a color checking module and an SVM detector, where the latter two stages further check whether the image patch that gets through the Adaboost detector is an LP. Test results of the dataset that consists of 950 real-world images show that the fusion reduces the false alarm rate. The proposed Fusion detector outperforms the conventional Adaboost detector throughout the ROC (Receiver Operating Characteristic) curve. The AUC (Area Under the Curve) of the best Fusion detector reaches 0.9081; however, the AUC of the best Adaboost detector is only 0.8441, which shows that the modification on feature extraction and the multistage information fusion significantly improve the LP detection performance.     

基于奇异值特征和隐马尔可夫模型的人脸检测

提出了基于奇异值特征和隐马尔可夫模型（HMM）的人脸检测方法,首先提出了基于奇异值特征和隐马尔可夫模型的正面端正人脸检测方法;然后将该算法扩展到检测任意旋转角度的人脸,其中正向端正人脸检测算法是通过隐马尔可夫模型来识别人脸／非人脸的奇异值特征,从而达到人脸检测的目的;扩展算法首无计算当前位置子图象窗口的奇异值特征向量,然后利用识别各个旋转角度人脸的HMM模型对之进行分类,以得到该子图象窗口的旋转角度,再经过旋正,重新再与识别正面端正人脸的HMM模型对, 此确定该子图象窗口是否为人脸,通过对一个由51幅集体照片组成的图象集进行测试,其中,正面端正人脸检测率为85．1％,而任意旋转角度的人脸检测率只有72．2％。     

Color image segmentation using automatic pixel classification with support vector machine

Automatic segmentation of images is a very challenging fundamental task in computer vision and one of the most crucial steps toward image understanding. In this paper, we present a color image segmentation using automatic pixel classification with support vector machine (SVM). First, the pixel-level color feature is extracted in consideration of human visual sensitivity for color pattern variations, and the image pixel's texture feature is represented via steerable filter. Both the pixel-level color feature and texture feature are used as input of SVM model (classifier). Then, the SVM model (classifier) is trained by using fuzzy c-means clustering (FCM) with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in compare with the state-of-the-art segmentation methods recently proposed in the literature.     

Fast and robust road sign detection in driver assistance systems

Road sign detection plays a critical role in automatic driver assistance systems. Road signs possess a number of unique visual qualities in images due to their specific colors and symmetric shapes. In this paper, road signs are detected by a two-level hierarchical framework that considers both color and shape of the signs. To address the problem of low image contrast, we propose a new color visual saliency segmentation algorithm, which uses the ratios of enhanced and normalized color values to capture color information. To improve computation efficiency and reduce false alarm rate, we modify the fast radial symmetry transform (RST) algorithm, and propose to use an edge pairwise voting scheme to group feature points based on their underlying symmetry in the candidate regions. Experimental results on several benchmarking datasets demonstrate the superiority of our method over the state-of-the-arts on both efficiency and robustness.     

Color image segmentation using pixel wise support vector machine classification

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. In this paper, we present a color image segmentation using pixel wise support vector machine (SVM) classification. Firstly, the pixel-level color feature and texture feature of the image, which is used as input of SVM model (classifier), are extracted via the local homogeneity model and Gabor filter. Then, the SVM model (classifier) is trained by using FCM with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.     

Study of generalized delay-timers in alarm configuration

In process plants, alarms are configured to notify operators of any abnormalities or faults. However, in practice a majority of raised alarms are false or nuisance and create problems for operators as they face an increasing number of alarms to handle. Adding delay-timers is a simple technique that can reduce this problem and is widely exercised in industry. In this work we propose a generalized delay-timer framework where instead of consecutive n samples in the conventional case, n₁ out of n consecutive samples (n₁ ≤ n) are considered to raise an alarm. For the generalized delay-timer, three important performance indices, namely, the false alarm rate (FAR), the missed alarm rate (MAR) and the expected detection delay (EDD), are calculated using Markov processes. Moreover, the performance and sensitivity of generalized delay-timers are compared with conventional delay-timers.     

一种改进的多源异构告警聚合方案

各类网络安全防御设备产生的大量冗余告警信息非常琐碎、误警率高, 给告警的分析和理解造成较大困难。针对这一问题进行研究, 提出一种改进的多源异构告警数据的聚合方案, 综合分析告警类型、源IP、目的IP、目的端口及时间间隔几个属性, 总结出四个规则, 并在聚合过程中动态更新时间间隔阈值, 提高聚合精确度。实验结果表明, 这种方法能高效减少异构告警信息的数量, 得到精简的超告警数据, 并实现了实时处理告警信息的能力。     

基于最弱前置条件的静态分析误报消除技术

针对程序静态分析技术误报过多的问题,提出一种基于最弱前置条件的静态分析误报消除方法。根据不同的软件安全性质,从目标状态出发,以需求驱动的方式得到过程起始位置的最弱前置条件,判断该条件公式的可满足性来消除误报。将该方法实例化来消除静态分析工具检测数组访问越界和空指针解引用的误报,实验结果表明该方法是有效且实用的。     

A novel low false alarm rate pedestrian detection framework based on single depth images

Pedestrian detection is an important image understanding problem with many potential applications. There has been little success in creating an algorithm which exhibits a high detection rate while keeping the false alarm in a relatively low rate. This paper presents a method designed to resolve this problem. The proposed method uses the Kinect or any similar type of sensors which facilitate the extraction of a distinct foreground. Then potential regions, which are candidates for the presence of human(s), are detected by employing the widely used Histogram of Oriented Gradients (HOG) technique, which performs well in terms of good detection rates but suffers from significantly high false alarm rates. Our method applies a sequence of operations to eliminate the false alarms produced by the HOG detector based on investigating the fine details of local shape information. Local shape information can be identified by efficient utilization of the edge points which, in this work, are used to formulate the so called Shape Context (SC) model. The proposed detection framework is divided in four sequential stages, with each stage aiming at refining the detection results of the previous stage. In addition, our approach employs a pre-evaluation stage to pre-screen and restrict further detection results. Extensive experimental results on the dataset created by the authors, involves 673 images collected from 11 different scenes, demonstrate that the proposed method eliminates a large percentage of the false alarms produced by the HOG pedestrian detector.     

Automatic multiple view inspection using geometrical tracking and feature analysis in aluminum wheels

The classic image processing method for flaw detection uses one image of the scene, or multiple images without correspondences between them. To improve this scheme, automated inspection using multiple views has been developed in recent years. This strategy’s key idea is to consider as real flaws those regions that can be tracked in a sequence of multiple images because they are located in positions dictated by geometric conditions. In contrast, false alarms (or noise) can be successfully eliminated in this manner, since they do not appear in the predicted places in the following images, and thus cannot be tracked. This paper presents a method to inspect aluminum wheels using images taken from different positions using a method called automatic multiple view inspection. Our method can be applied to uncalibrated image sequences, therefore, it is not necessary to determine optical and geometric parameters normally present in the calibrated systems. In addition, to improve the performance, we designed a false alarm reduction method in two and three views called intermediate classifier block (ICB). The ICB method takes advantage of the classifier ensemble methodology by making use of feature analysis in multiple views. Using this method, real flaws can be detected with high precision while most false alarms can be discriminated.     

Real-time video fire smoke detection by utilizing spatial-temporal ConvNet features

Fire is one of the most dangerous disasters threatening human life and property globally. In order to reduce fire losses, researches on video analysis for early smoke detection have become particularly significant. However, it is still a challenging task to extract stable features for smoke recognition, largely due to its variations in color, shapes and texture. Classical convolutional neural networks can automatically learn feature representations of appearance from a single frame but fail to capture motion information between frames. For addressing this issue, in this paper, we propose a spatial-temporal based convolutional neural network for video smoke detection, and for real-time detection, propose an enhanced architecture, which utilizes a multitask learning strategy to jointly recognize smoke and estimate optical flow, capturing intra-frame appearance features and inter-frame motion features simultaneously. The effectiveness and efficiency of our proposed method is validated by experiments carried out on our self-created dataset, which achieves 97.0% detection rate and 3.5% false alarm rate with processing time of 5ms per frame, obviously outperforming existing methods.     

Color texture segmentation based on image pixel classification

Image segmentation partitions an image into nonoverlapping regions, which ideally should be meaningful for a certain purpose. Thus, image segmentation plays an important role in many multimedia applications. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. By combination of Fuzzy Support Vector Machine (FSVM) and Fuzzy C-Means (FCM), a color texture segmentation based on image pixel classification is proposed in this paper. Specifically, we first extract the pixel-level color feature and texture feature of the image via the local spatial similarity measure model and localized Fourier transform, which is used as input of FSVM model (classifier). We then train the FSVM model (classifier) by using FCM with the extracted pixel-level features. Color image segmentation can be then performed through the trained FSVM model (classifier). Compared with three other segmentation algorithms, the results show that the proposed algorithm is more effective in color image segmentation.     

基于图像纹理的印刷缺陷检测

针对传统点对点印刷缺陷检测存在经常误报的情况,提出了一种基于图像纹理的印刷缺陷检测模型,该模型经实验证明,具有稳定性高,误报率少的优点。     

一种改进的基于MRF模型的斑块检测算法

斑块是一种常见的电影胶片损伤.提出了一种基于马尔可夫随机场(MRF)的改进的多步斑块检测及验证算法(MDV).MDV检测算法分3个步骤.第1步将斑点检测索引算法(SDIp)和等级顺序差分检测算法(ROD)相结合以提高斑块检测算法的检全率.第2步的改进的MRF算法以第1步检测结果为运算定义域,大大减小了MRF算法的运算量.第2步随后在原始帧和运动补偿帧分别进行MRF算法检测,并通过添加去噪因子降低了改进的MRF算法的误检率.第3步通过时域的匹配技术将斑块进一步去伪存真.实验结果表明,在与现有的算法的对比中,该方法不仅有着更高的检全率和更低的误检率,而且计算速度也大大提高.     

基于高效核偏最小二乘的质量相关故障检测

核偏最小二乘(KPLS)是一种多元统计方法, 广泛应用于过程监控, 然而, KPLS采用斜交分解, 导致质量相关空间存在冗余信息易引发误报警. 因此, 本文提出了高效核偏最小二乘(EKPLS)模型, 所提方法通过奇异值分解(SVD)将核矩阵正交分解为质量相关空间和质量无关空间, 有效降低质量相关空间中的冗余信息, 并采用主成分分析(PCA)按方差大小将质量相关空间分解为质量主空间和质量次空间. 此外, 为进一步降低由质量无关故障引发的误报警, 提出基于质量估计的正交信号修正(OSC)预处理方法, 并结合EKPLS模型提出了OSC-EKPLS算法. OSCEKPLS通过质量估计值对被测数据进行OSC预处理, 降低了计算复杂度和误报率. 最后, 通过数值仿真和田纳西–伊斯曼过程验证了OSC-EKPLS具有良好的故障检测性和更低的误报率.