基于改进混合高斯建模和短时稳定度的遗留物检测算法

传统遗留物检测算法存在算法过于复杂和环境适应性差的局限。本文将改进的混合高斯建模方法应用于遗留物检测,利用背景匹配失败时生成的前景模型进行前景匹配并引入短时稳定度指标,在深入挖掘前景模型中包含的遗留物信息和像素点级目标状态信息的基础上对遗留物进行综合判断。文中详细分析了传统方法的性能局限并阐述了新方法中前景模型和短时稳定度的作用原理同时给出了具体的算法流程。多场景下的实验分析表明,增加对前景模型的考察使算法在保留传统方法优点的同时具备了良好的遗留物检测能力,而短时稳定度的引入则能够进一步降低传统方法中前景模型向背景模型转换的风险。对比实验结果中本文方法在表现出良好环境适应性的同时误检团块数明显低于其他方法,算法在复杂背景条件下达到了良好的检测性能。      

Improvement of Honey Bee Tracking on 2D Video with Hough Transform and Kalman Filter

This paper introduces a novel approach to improve bee tracking. For this research, 50 Hz 2D videos have been recorded. The first part of the model tracks bees using a method combining colour thresholding and foreground detection, and then produces the bees’ shapes as blobs on a binary image. These blobs are analysed to estimate the positions and directions of motion of the bees. However, when bees cross over one another in the image, they are hard to track. This paper tackles this problem by using the standard Hough transform applied to bee research. Then a Kalman filter is used to track the bees using their estimated position information. Because of the 50 Hz frame rate, the trajectories of the bee movements are too variable to track reliably. The Kalman filter is modified to fit this situation. Multiple bees are being tracked, so the Hungarian assignment algorithm is used to assign predictions and measurements to individual bees. The experiment shows the bees are reliably tracked in the close view 50 Hz 2D video.     

Efficient mobility prediction scheme for pervasive networks

This paper aims towards probabilistic reasoning and Bayesian‐based recommendations to predict the next movement of a person. The proposed model in this work observes the behavior and movement patterns of humans for a day both at home and at their office to predict their future activities. To achieve this, an efficient model has been designed that provides the probable context‐based location of a person and predicts his next movement based on his behavior on some particular day at a particular time. The proposed model allows ubiquitous services to adapt to uncertain situations in today's world using different mechanisms such as monitoring the human behavior patterns and evaluating the user preferences and profiles. A case study of the office activity chart has been provided, and based on the experimentation performed on the related events, the probability in evaluating some “N”chained events of a person in a consecutive order using the proposed model has been found to be 0.002, which infers that there are fewer chances that the person will perform the same particular sequence of events.     

Activity density map visualization and dissimilarity comparison for eldercare monitoring

In this paper, we present a methodology for analyzing passive infrared motion sensor data logged in the homes of seniors. The objective is to capture activity patterns that represent different health conditions. Recognizing changes in the activity patterns can then be used to provide early detection of health changes. A visualization of motion sensor data is introduced in the form of a density map that uses different colors to show varying levels of activity. For evaluating the activity density level accurately, time away from home is determined first using a system of fuzzy rules. In addition, a dissimilarity between two density maps is computed using texture features for automatically determining changes in activity patterns, which may indicate a health problem. The activity density maps are being used in an aging in place senior housing community to aid clinicians in early illness detection. Three case studies of elderly residents are included to illustrate how the density map and dissimilarity measure can be used to track general activity level and daily patterns over time, showing changes in physical, cognitive, and mental health.     

Image quality assessment based on regions of interest

Most methods in the literature of image quality assessment (IQA) use whole image information for measuring image quality. However, human perception does not always use this criterion to assess the quality of images. Individuals usually provide their opinions by considering only some parts of an image, called regions of interest. Based on this hypothesis, in this research work, a segmentation technique is initially employed to obtain a bi-level image map composed of the foreground and background information. A patch selection strategy is then proposed to choose some particular patches based on the foreground information as the regions of interest for IQA. Three recent IQA methods in the literature are considered to demonstrate the improvement in IQA when using only the extracted regions of interest. To evaluate the impact of the proposed patch selection strategy in various IQA metrics, three publicly available datasets were used for experiments. Experimental results have revealed that our proposal, based on the regions of interest, can improve quality measures of three IQA methods.     

旋转式RCS可变角反射器的结构设计与可行性研究

导体金属角反射器是雷达无源干扰的重要防御手段,但目前使用的角反射器,其雷达截面积（RCS）值固定不变,伪装欺骗的类型有限。采用定性分析和仿真实验相结合的方法,设计了一款旋转式RCS可变角反射器,同一反射器可以形成不同的RCS值,伪装不同类型的目标,具有良好的应用前景。     

"电磁场理论"课程教学中两个实例的应用

“电磁场理论”课程是信息工程专业和电气工程及自动化专业的一门重要的专业基础课,该课程是有名的难学课程,学生在学习的过程中畏难情绪大,没有积极性。为激发学生学习“电磁场理论”课程的兴趣,提高教学效果,在授课过程中增加电磁场的应用实例是有效的手段。本文给出了教学过程中引用的两个实例：用介质中电磁波传播理论,分析了矿井煤岩介质中电磁波的传播特性,给出了有用的结论,使学生看到了电磁场理论的应用前景,提高了学习的兴趣,收到了很好的效果。     

Activity modeling using event probability sequences.

Changes in motion properties of trajectories provide useful cues for modeling and recognizing human activities. We associate an event with significant changes that are localized in time and space, and represent activities as a sequence of such events. The localized nature of events allows for detection of subtle changes or anomalies in activities. In this paper, we present a probabilistic approach for representing events using the hidden Markov model (HMM) framework. Using trained HMMs for activities, an event probability sequence is computed for every motion trajectory in the training set. It reflects the probability of an event occurring at every time instant. Though the parameters of the trained HMMs depend on viewing direction, the event probability sequences are robust to changes in viewing direction. We describe sufficient conditions for the existence of view invariance. The usefulness of the proposed event representation is illustrated using activity recognition and anomaly detection. Experiments using the indoor University of Central Florida human action dataset, the Carnegie Mellon University Credo Intelligence, Inc., Motion Capture dataset, and the outdoor Transportation Security Administration airport tarmac surveillance dataset show encouraging results.     

Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach

This paper presents an efficient algorithm for segmenting different types of pulmonary nodules including high and low contrast nodules, nodules with vasculature attachment, and nodules in the close vicinity of the lung wall or diaphragm. The algorithm performs an adaptive sphericity oriented contrast region growing on the fuzzy connectivity map of the object of interest. This region growing is operated within a volumetric mask which is created by first applying a local adaptive segmentation algorithm that identifies foreground and background regions within a certain window size. The foreground objects are then filled to remove any holes, and a spatial connectivity map is generated to create a 3-D mask. The mask is then enlarged to contain the background while excluding unwanted foreground regions. Apart from generating a confined search volume, the mask is also used to estimate the parameters for the subsequent region growing, as well as for repositioning the seed point in order to ensure reproducibility. The method was run on 815 pulmonary nodules. By using randomly placed seed points, the approach was shown to be fully reproducible. As for acceptability, the segmentation results were visually inspected by a qualified radiologist to search for any gross misssegmentation. 84% of the first results of the segmentation were accepted by the radiologist while for the remaining 16% nodules, alternative segmentation solutions that were provided by the method were selected.     

Novelty detection using extreme value statistics

Extreme value theory is a branch of statistics that concerns the distribution of data of unusually low or high value, i.e. in the tails of some distribution. These extremal points are important in many applications as they represent the outlying regions of normal events against which we may wish to define abnormal events. In the context of density modelling, novelty detection or radial-basis function systems, points that lie outside of the range of expected extreme values may be flagged as outliers. There has been interest in the area of novelty detection, but decisions as to whether a point is an outlier or not tend to be made on the basis of exceeding some (heuristic) threshold. It is shown that a more principled approach may be taken using extreme value statistics     

Robust human detection within a highly dynamic aquatic environment in real time.

This paper presents a real-time foreground detection method for monitoring swimming activities at an outdoor swimming pool. Robust performance and high accuracy of detecting objects-of-interest are two central issues of concern. Therefore, in this paper, a considerable amount of attention has been placed on the following aspects: 1) to establish a better method of modeling aquatic background, which exhibitis dynamic characteristics with random spatial movements, and 2) to establish a method of enhancing the visibility of the foreground by removing specular reflection at nighttime. First, the development of a new background modeling method is reported. In the proposed approach, the background is modeled as a composition of homogeneous blob movements. With an implementation of a spatial searching process, the proposed method shows capability in associating and distinguishing movements caused by the background. Hence, this contributes to better performance in foreground detection. On the issue of enhancing the visibility of the foreground, a decision-based filtering scheme is proposed as a preprocessing step. A defined concept term, fluctuation measure, is defined for classifying each pixel to be one of the predefined types. This has allowed suitable spatial or spatiotemporal filters to be applied accordingly for color the compensation step. All of these developments are evaluated by testing live on a busy Olympic-size outdoor public swimming pool. Both qualitative and quantitative evaluations are reported. This provides a comprehensive study of the system.     

Moving foreground object detection via robust SIFT trajectories

In this paper, we present an automatic foreground object detection method for videos captured by freely moving cameras. While we focus on extracting a single foreground object of interest throughout a video sequence, our approach does not require any training data nor the interaction by the users. Based on the SIFT correspondence across video frames, we construct robust SIFT trajectories in terms of the calculated foreground feature point probability. Our foreground feature point probability is able to determine candidate foreground feature points in each frame, without the need of user interaction such as parameter or threshold tuning. Furthermore, we propose a probabilistic consensus foreground object template (CFOT), which is directly applied to the input video for moving object detection via template matching. Our CFOT can be used to detect the foreground object in videos captured by a fast moving camera, even if the contrast between the foreground and background regions is low. Moreover, our proposed method can be generalized to foreground object detection in dynamic backgrounds, and is robust to viewpoint changes across video frames. The contribution of this paper is trifold: (1) we provide a robust decision process to detect the foreground object of interest in videos with contrast and viewpoint variations; (2) our proposed method builds longer SIFT trajectories, and this is shown to be robust and effective for object detection tasks; and (3) the construction of our CFOT is not sensitive to the initial estimation of the foreground region of interest, while its use can achieve excellent foreground object detection results on real-world video data.     

一种融合预测过采样的运动目标检测算法

为了改善运动目标检测的精度,提出了一种融合了预测过采样的运动目标检测新方法.首先,基于二维傅里叶变换预测当前帧的目标形状并计算形状相似度;然后,从历史检测结果中选择一定数量的参考帧,使用光流法跟踪目标像素点在参考帧与当前帧之间的运动轨迹,并以像素点轨迹为参考在采样区间执行稠密过采样;最后,基于过采样样本构造前景模型,并在图分割框架内联合使用前景背景模型实现目标检测.在公共数据与自采数据集上对所提方法进行了实验验证,结果表明,相对于经典的运动目标检测算法,所提方法能够有效提高检测精度.     

Vehicle Tracking and Speed Measurement system (VTSM) based on novel feature descriptor: Diagonal Hexadecimal Pattern (DHP)

Object detection and tracking is an important and active research area in computer vision community. The proposed Vehicle Tracking and Speed Measurement (VTSM) system can find out speed parameters of the vehicles. Speed parameters are used to take judgment on accidents at a low cost. The main objective of this paper is to develop an algorithm that can detect foreground, track specified object and calculate speed parameter of the object. Identifying stationary background from moving objects in a video is a critical task. To achieve superior foreground detection quality across unconstrained scenarios, a novel dynamic background subtraction and object tracking algorithm using a novel Diagonal Hexadecimal Pattern (DHP) is proposed. Metric F-score and MOTA are used to measure the performance of the proposed system. From the results, it is observed that the proposed system gives good results for the background subtraction and tracking.     

一种分阶段的形态学图像分割算法

图像分割算法的研究是计算机图像处理与识别技术的关键和难点所在。文中提出的分割算法可分为两步：首先运用基于数学形态学的预处理把图像中的前景简化,同时保留背景信息;然后通过采用上下游决策的改进算法来分割图像,以得到最终结果。针对医学图像样本进行实验,实验结果表明了算法的有效性。     

Error-probability bounds for continuous-time decision problems

The evaluation of error-probability bounds for binary detection problems involving continuons-time stochastic processes as signals is considered. These bounds are of interest because, in even the simplest detection problems, the computation of the exact probabilities of error is usually mathematically intractable. The method used consists of applying some results from martingale theory to detection and estimation problems. Only discontinuous observations that contain the rate process associated with a counting process are considered. The problem addressed is to evaluate Chernoff bounds on error probabilities for the likelihood-ratio test. The solution procedure consists of a measure transformation technique that makes it possible to obtain an expression for the Chernoff bound in terms of an expectation of a multiplicative functional of the conditional mean signal (rate process) estimates. If the processes involved are Markov, it is then possible to represent the above expression as a solution to a partial differential equation that is derived from the backward equation of Kolmogorov. The above procedure is repeated when the optimal estimates are replaced by suboptimal estimates. Examples are given to illustrate the technique.     

Inter-vehicle communication of warning information: an experimental study

In this paper, we show experimentally the feasibility of intervehicle communication of warning information. Warning messages convey significant information that might improve the safety of drivers and passengers. Intervehicle communication can be achieved by the detection of important events through a vision-based detection module, and sharing them between vehicles using a transmission module. In this paper, we developed a testbed that considers both modules in order to detect, recognize and share relevant information, such as traffic signs. To the best of our knowledge, our architecture is the first that combines detection and transmission of messages in the same platform. We detect traffic signs as blobs using the Maximally Stable Extremal Regions (MSER) algorithm, and we recognize them using Random forest classifiers. In the transmission module, we used a simplied broadcasting mechanism that avoids the use of handshaking to establish a communication. In order to assess our system, a set of indoor and outdoor experiments are considered.     

Towards connecting people,locations and real-world events in a cellular network

The success of personal mobile communication technologies has led an emerging expansion of the telecommunication infrastructure but also to an explosion to mobile broadband data traffic as more and more people completely rely on their mobile devices, either for work or entertainment. The continuously interaction of their mobile devices with the mobile network infrastructure creates digital traces that can be easily logged by the network operators. These digital traces can be further used, apart from billing and resource management, for large-scale population monitoring using mobile traffic analysis. They could be integrated into intelligent systems that could help at detecting exceptional events such as riots, protests or even at disaster preventions with minimal costs and improve people safety and security, or even save lives. In this paper we study the use of fully anonymized and highly aggregate cellular network data, like Call Detail Records (CDRs) to analyze the telecommunication traffic and connect people, locations and events. The results show that by analyzing the CDR data exceptional spatio-temporal patterns of mobile data can be correlated to real-world events. For example, high user network activity was mapped to religious festivals, such as Ramadan, Le Grand Magal de Touba and the Tivaouane Maouloud festival. During the Ramadan period it was noticed that the communication pattern doubled during the night with a slow start during the morning and along the day. Furthermore, a peak increase in the number of voice calls and voice calls duration in the area of Kafoutine was mapped to the Casamance Conflict in the area which resulted in four deaths. Thus, these observations could be further used to develop an intelligent system that detects exceptional events in real-time from CDRs data monitoring. Such system could be used in intelligent transportation management, urban planning, emergency situations, network resource allocation and performance optimization, etc.     

Stability in contractive nonlinear neural networks

We consider models of the form mu chi = -x + p + WF(x) where x = x(t) is a vector whose entries represent the electrical activities in the units of a neural network. W is a matrix of synaptic weights, F is a nonlinear function, and p is a vector (constant or slowly varying over time) of inputs to the units. If the map WF(x) is a contraction, then the system has a unique equilibrium which is globally asymptotically stable; consequently the network acts as a stable encoder in that its steady-state response to an input is independent of the initial state of the network. We consider some relatively mild restrictions on W and F(x), involving the eigenvalues of W and the derivative of F, that are sufficient to ensure that WF(x) is a contraction. We show that in the linear case with spatially-homogeneous synaptic weight, the eigenvalues of W are simply related to the Fourier transform of the connection pattern. This relation makes it possible, given cortical activity patterns as measured by autoradiographic labeling, to construct a pattern of synaptic weights which produces steady state patterns showing similar frequency characteristics. Finally, we consider the relationships, in the spatial and frequency domains, between the equilibrium of the model and that of the linear approximation mu chi = -x + p + Wx; this latter equilibrium can be computed easily from p in the homogeneous case using discrete Fourier transforms.