基于红外图像的地面自主车导航的道路边界检测

讨论了用于地面自主车（ALV）视觉导航的红外道路图像的特点,论述了红外道路图像的预处理策略和方法,分析了一些常用的图像处理方法在处理红外道路图像时的缺陷,指出由于温差的渐变性。红外道路图像的路边检测特别适合使用阈值穷举法．并给出了在温度渐变的路边上边缘强度的求取方法和使用二阶微分算子得到边界线段的精确位置的方法。同时使用了边缘信息和灰度信息来提取道路边界信息,阈值穷举法和二阶微分算子得到的道路边界线段相互融合。得到候选的道路边界线段,在ALV的实际应用中取得了很好的效果。     

Superpixel based color contrast and color distribution driven salient object detection

Color is the most informative low-level feature and might convey tremendous saliency information of a given image. Unfortunately, color feature is seldom fully exploited in the previous saliency models. Motivated by the three basic disciplines of a salient object which are respectively center distribution prior, high color contrast to surroundings and compact color distribution, in this paper, we design a comprehensive salient object detection system which takes the advantages of color contrast together with color distribution and outputs high quality saliency maps. The overall procedure flow of our unified framework contains superpixel pre-segmentation, color contrast and color distribution computation, combination, and final refinement.In color contrast saliency computation, we calculate center-surrounded color contrast and then employ the distribution prior in order to select correct color components. A global saliency smoothing procedure that is based on superpixel regions is introduced as well. This processing step preferably alleviates the saliency distortion problem, leading to the entire object being highlighted uniformly. Finally, a saliency refinement approach is adopted to eliminate artifacts and recover unconnected parts within the combined saliency maps.In visual comparison, our method produces higher quality saliency maps which stress out the total object meanwhile suppress background clutter. Both qualitative and quantitative experiments show our approach outperforms 8 state-of-the-art methods, achieving the highest precision rate 96% (3% improvement from the current highest), when evaluated via one of the most popular data sets. Excellent content-aware image resizing also could be achieved using our saliency maps.     

基于边缘强度的红外图像阈值分割方法研究

提出一种针对复杂场景的基于图像边缘强度的阈值分割算法。首先进行边缘检测,然后根据边缘强度进行像素灰度值加权平均计算出图像的分割阈值。该算法简单、实用,可对海上及空中目标进行准确分割,并已在实际的跟踪系统中进行了应用,结果表明目标分割正确,系统实时性强、稳定可靠。     

激光雷达含噪图像边缘检测算法比较

边缘检测是激光雷达图像处理的重要组成部分,其性能直接影响整个雷达系统的精度和性能。对Sobel、Kirsch、LOG、Canny、参数检验算子等边缘检测算法原理及其性能参数进行了分析和比较,并且对含有散斑噪声的激光雷达图像边缘检测进行了实验研究,结果表明,参数检验算法在受散斑噪声影响的图像中的检测性能较好。     

小波变换的自适应阈值图像边缘检测方法

在Marr的计算机视觉系统中,图像边缘检测占据着重要位置。但由于问题本身的复杂性和技术手段的限制,图像边缘检测的研究困难重重。近10年来,由于小波分析技术在工具和数学方法上的重大突破,试图将小波理论应用于图像边缘检测。根据边缘检测的评价标准,参照最佳边缘滤波器的设计要求,确定选择用于边缘检测的小波母函数的一般准则,并在此基础上构造出二次B样条小波,提出了基于小波变换的自适应阈值图像边缘检测的新方法。通过计算机仿真对该算法进行了验证,结果成于采用固定阈值的小波边缘检测。     

A novel FPGA-based architecture for Sobel edge detection operator

A novel FPGA-based architecture for Sobel edge detection algorithm has been proposed. The Sobel algorithm is chosen due to its property of providing a differencing as well as noise smoothing operation in the single kernel. Thus, noise sensitivity of first gradient based operations can be avoided by the use of this algorithm. The implementation of edge detection algorithms on a field programmable gate array (FPGA) is motivated by the fact that large memory FPGAs are now available, providing a platform for processing real time algorithms on application-specific hardware with substantially higher performance than programmable digital signal processors (DSPs). This architecture can be used as a building block of a pattern recognition system, autonomous robot navigation, and also as a system for creating an image dazzling effect in multimedia graphics. This architecture is implicitly pipelined to provide a system capable of operating at a clock speed of 99.499?MHz which is a significant improvement over programmable DSPs implementation.     

Spatio-temporal crowd density model in a human detection and tracking framework

Recently significant progress has been made in the field of person detection and tracking. However, crowded scenes remain particularly challenging and can deeply affect the results due to overlapping detections and dynamic occlusions. In this paper, we present a method to enhance human detection and tracking in crowded scenes. It is based on introducing additional information about crowds and integrating it into the state-of-the-art detector. This additional information cue consists of modeling time-varying dynamics of the crowd density using local features as an observation of a probabilistic function. It also involves a feature tracking step which allows excluding feature points attached to the background. This process is favorable for the later density estimation since the influence of features irrelevant to the underlying crowd density is removed. Our proposed approach applies a scene-adaptive dynamic parametrization using this crowd density measure. It also includes a self-adaptive learning of the human aspect ratio and perceived height in order to reduce false positive detections. The resulting improved detections are subsequently used to boost the efficiency of the tracking in a tracking-by-detection framework. Our proposed approach for person detection is evaluated on videos from different datasets, and the results demonstrate the advantages of incorporating crowd density and geometrical constraints into the detection process. Also, its impact on tracking results have been experimentally validated showing good results.     

运动目标检测与识别方法研究

为了解决低对比度红外序列图像中运动小目标的检测问题,提出了一种基于多级滤波的检测方法.首先,对具有一定空间分布范围(≤5×5)的小目标,可根据目标/噪声/背景的灰度分布模型具体分析其频谱特性范围.由Fourier变换定义可知,噪声能量主要集中在高频段,背景能量主要集中在低频段,而目标能量则主要分布在中频段,所以可以根据目标的大小,由门函数的Fourier变换估算出各种尺寸目标所处的频段,然后选择不同级数的滤波器将不同大小的候选目标从复杂背景中分离出来.经过多级滤波处理后,再对得到的包含候选目标的图像进行图像分割等适当的后续处理,就可以检测出真正的目标.     

A perceptual scheme for fully automatic video shot boundary detection

In this paper, we propose a novel and robust modus operandi for fast and accurate shot boundary detection where the whole design philosophy is based on human perceptual rules and the well-known “Information Seeking Mantra”. By adopting a top–down approach, redundant video processing is avoided and furthermore elegant shot boundary detection accuracy is obtained under significantly low computational costs. Objects within shots are detected via local image features and used for revealing visual discontinuities among shots. The proposed method can be used for detecting all types of gradual transitions as well as abrupt changes. Another important feature is that the proposed method is fully generic, which can be applied to any video content without requiring any training or tuning in advance. Furthermore, it allows a user interaction to direct the SBD process to the user's “Region of Interest” or to stop it once satisfactory results are obtained. Experimental results demonstrate that the proposed algorithm achieves superior computational times compared to the state-of-art methods without sacrificing performance.     

Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse

Edge-computing-enabled smart greenhouses are a representative application of the Internet of Things (IoT) technology, which can monitor the environmental information in real-time and employ the information to contribute to intelligent decision-making. In the process, anomaly detection for wireless sensor data plays an important role. However, the traditional anomaly detection algorithms originally designed for anomaly detection in static data do not properly consider the inherent characteristics of the data stream produced by wireless sensors such as infiniteness, correlations, and concept drift, which may pose a considerable challenge to anomaly detection based on data stream and lead to low detection accuracy and efficiency. First, the data stream is usually generated quickly, which means that the data stream is infinite and enormous. Hence, any traditional off-line anomaly detection algorithm that attempts to store the whole dataset or to scan the dataset multiple times for anomaly detection will run out of memory space. Second, there exist correlations among different data streams, and traditional algorithms hardly consider these correlations. Third, the underlying data generation process or distribution may change over time. Thus, traditional anomaly detection algorithms with no model update will lose their effects. Considering these issues, a novel method (called DLSHiForest) based on Locality-Sensitive Hashing and the time window technique is proposed to solve these problems while achieving accurate and efficient detection. Comprehensive experiments are executed using a real-world agricultural greenhouse dataset to demonstrate the feasibility of our approach. Experimental results show that our proposal is practical for addressing the challenges of traditional anomaly detection while ensuring accuracy and efficiency.     

心电采集电路的设计与实现

介绍一种心电采集检测电路的设计,该设计以AD620和OP07为核心元件,针对心电信号的组成和干扰频率范围,进行了分析,对由电极采集到的心电信号,通过前置放大电路将微弱的心电心电信号放大,并通过低通滤波器、高通滤波器、及50Hz陷波电路滤除干扰最后通过后置放大电路进一步放大得到清晰的心电波形。系统具有高输入阻抗、高共模抑制比、低噪声、低漂移、和高信噪比、成本低等优点。     

以观测器为基础的量化网络化系统故障检测

文中针对具有未知输入和不确定扰动信号的系统,研究一类以观测器为基础的量化网络化系统故障检测问题。首先,引入时变量化器,对输出信号进行量化处理,在此基础上,对原系统建立故障检测滤波器,通过比较原系统与观测器的输出,建立故障检测滤波器误差系统。最后,基于H∞稳定性理论,将故障检测滤波器问题转化为问题。在论证过程中结合LMI有关定理,给出仿真实例,验证文中方法的有效性。     

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.     

Combining texture and stereo disparity cues for real-time face detection

Intuitively, integrating information from multiple visual cues, such as texture, stereo disparity, and image motion, should improve performance on perceptual tasks, such as object detection. On the other hand, the additional effort required to extract and represent information from additional cues may increase computational complexity. In this work, we show that using biologically inspired integrated representation of texture and stereo disparity information for a multi-view facial detection task leads to not only improved detection performance, but also reduced computational complexity. Disparity information enables us to filter out 90% of image locations as being less likely to contain faces. Performance is improved because the filtering rejects 32% of the false detections made by a similar monocular detector at the same recall rate. Despite the additional computation required to compute disparity information, our binocular detector takes only 42 ms to process a pair of 640×480 images, 35% of the time required by the monocular detector. We also show that this integrated detector is computationally more efficient than a detector with similar performance where texture and stereo information is processed separately.     

一个检测分布式攻击的入侵检测模型的设计

给出了一个检测分布式攻击的入侵检测系统模型的设计，该模型采用基于特征的方法，能够实现数据收集方法在单独场所所不能实现的对分布式攻击的检测。跟其他方法相比，该方法能够极大的降低入侵检测式的通信量，从而简化了通讯安全管理的复杂性。     

天基红外扫描图像点目标检测算法

针对天基红外扫描型探测器的成像特点(时间延迟积分和半像元错列对准),提出了一种基于自适应背景预测的红外扫描图像点目标检测算法。首先,采用递归背景估计的背景预测模型,利用最速下降法求解滤波系数。其次,对背景去除后的残差图像进行自适应门限探测,并对过门限图像进行双向脉冲匹配以抑制噪声,提取目标。最后,采用蒙特卡罗方法对算法性能进行了仿真分析。实验结果表明:当信噪比大于3时,目标检测概率可达99.5%(虚警1.07×10-2)。算法实时性分析表明:算法处理能力为31.37Mb/s。     

基于特征帧构建的运动目标检测方法

非参数密度估计在样本分析建模方面得到了很大的关注,尤其是核密度估计方法。但由于核密度估计方法计算量大,应用到运动目标检测方面很难达到实时效果。提出了一种特征帧构建的核密度估计方法。因为核密度估计不需要假设背景模型的密度分布函数,所有样本值又满足独立同分布的原则,所以可以通过特征帧构建的方法进行背景建模,同时应用此方法进行背景更新。实验结果表明:该方法能够适应环境变化且具有运算速度快、实时性好等特点,可以将其应用到复杂背景下的监控系统中。     

基于YOLOv5网络模型的人员口罩佩戴实时检测

近年来,随着硬件算力的提升和人工智能算法的创新发展,使得深度学习算法在目标检测方面有着广泛的应用.针对现有人工方式查看人员口罩佩戴情况的不足,提出了一种基于深度学习YOLOv5算法实现对口罩佩戴情况的实时检测.算法首先将数据集进行归一化处理,再将数据接入YOLOv5网络进行迭代训练,并将最优权重数据保存用作测试集测试,...