The power-saving mechanism for solar power LED traffic signal light system

This article proposes a mechanism to reduce the battery power consumed by an independent solar-powered light intensity detection (LED) used for auxiliary traffic sign lighting by means of special background light sensor detection and an LED brightness control. This kind of traffic sign is deployed in remote areas and public utilities, where workers find hard to reach. Symbols and signs shaped by LED lights can assist drivers in mountain areas, remote areas, roads and highways with indications of directions and/or speed limits such that traffic accidents can be reduced. LED lights are especially suitable for environments with poor lights.     

Classification images for chromatic signal detection

The number and nature of the mechanisms for the detection of colored stimuli are still unclear. We use the paradigm of classification images to investigate the detection of a signal of homogeneous color added to a noisy texture. Both signal and noise colors were chosen from the isoluminant plane of the Derrington-Krauskopf-Lennie (DKL) color space. The signal consisted of a square of homogeneous color that was chosen from either cardinal or noncardinal directions of the DKL color space. The noisy texture consisted of small squares of varying colors that were chosen randomly across the isoluminant plane. Classification images reveal that (1) the cardinal axes play no specific role; (2) the widths of the tuning curves vary between 30 and 90 deg, consistent with the variation of tuning widths of neurons at early cortical stages; and (3) detection is not based on the whole region covered by the signal but is influenced mostly by a small spot around the fixation point.     

Spatial processing in color reproduction

We consider the reproduction of color subject to material and neighborhood constraints. By "material constraints," we mean any constraints that are applied to the amount of ink, lights, voltages, and currents that are used in the generation of color. In the first instance we consider the problem of reproducing a target color constrained by maximum additive color signals, such as in the phosphorescence process in a cathode ray tube. In the second instance we consider the more difficult problem of reproducing color subject to constraints on the maximum primary color variations in a (spatial) neighborhood. We introduce the idea of adjacent color variance (ACV) and then attempt to reproduce colors subject to an upper bound on the ACV. An algorithm that is suitable for this task is the method of vector space projections (VSP). In order to use VSP for constrained color reproduction, we use a novel approach to linearize nonlinear CIE-Lab space constraints. Experimental results are furnished that demonstrate that using the ACV as a bound helps to reduce reproduction artifacts in a color image.     

Digital Forensics for Recoloring via Convolutional Neural Network

As a common medium in our daily life, images are important for most people to gather information. There are also people who edit or even tamper images to deliberately deliver false information under different purposes. Thus, in digital forensics, it is necessary to understand the manipulating history of images. That requires to verify all possible manipulations applied to images. Among all the image editing manipulations, recoloring is widely used to adjust or repaint the colors in images. The color information is an important visual information that image can deliver. Thus, it is necessary to guarantee the correctness of color in digital forensics. On the other hand, many image retouching or editing applications or software are equipped with recoloring function. This enables ordinary people without expertise of image processing to apply recoloring for images. Hence, in order to secure the color information of images, in this paper, a recoloring detection method is proposed. The method is based on convolutional neural network which is quite popular in recent years. Unlike the traditional linear classifier, the proposed method can be employed for binary classification as well as multiple labels classification. The classification performance of different structure for the proposed architecture is also investigated in this paper.     

Scale and skew‐invariant road sign recognition

A fast and robust method to detect and recognize scaled and skewed road signs is proposed in this paper. In the detection stage, the input color image is first quantized in HSV color model. Border tracing those regions with the same colors as road signs is adopted to find the regions of interest (ROI). The ROIs are then automatically adjusted to fit road sign shape models so as to facilitate detection verification even for scaled and skewed road signs in complicated scenes. Moreover, the ROI adjustment and verification are both performed only on border pixels; thus, the proposed road sign detector is fast. In the recognition stage, the detected road sign is normalized first. Histogram matching based on polar mesh is then adopted to measure the similarity between the scene and model road signs to accomplish recognition. Since histogram matching is fast and has high tolerance to distortion and deformation while contextual information can still be incorporated into it in a natural and elegant way, our method has high recognition accuracy and fast execution speed. Experiment results show that the detection rate and recognition accuracy of our method can achieve 94.2% and 91.7%, respectively. On an average, it takes only 4–50 and 10 ms for detection and recognition, respectively. Thus, the proposed method is effective, yet efficient. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 28–39, 2007     

基于直线包络的交通标志快速定位方法

为了提高快速定位交通标志的准确性,通过对场景中复杂的直线信息以及颜色信息的融合,提出了一种基于直线包络的快速定位交通标志的方法。该方法首先利用颜色信息以及统计直线信息获取目标候选区域,将交通标志进行粗定位,然后利用形态学运算获取连通区域,再利用交通标志的纵横比和内部孔洞信息将交通标志从目标候选区域中准确定位出来。实验表明该方法可以提高交通标志在夜晚等复杂背景中定位的准确性。     

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique.     

Traffic Sign Detection with Low Complexity for Intelligent Vehicles Based on Hybrid Features

Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians. Consequently, traffic signs have been of great importance for every civilized country, which makes researchers give more focus on the automatic detection of traffic signs. Detecting these traffic signs is challenging due to being in the dark, far away, partially occluded, and affected by the lighting or the presence of similar objects. An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues. This technique aimed to devise an efficient, robust and accurate approach. To attain this, initially, the approach presented a new formula, inspired by existing work, to enhance the image using red and green channels instead of blue, which segmented using a threshold calculated from the correlational property of the image. Next, a new set of features is proposed, motivated by existing features. Texture and color features are fused after getting extracted on the channel of Red, Green, and Blue (RGB), Hue, Saturation, and Value (HSV), and YCbCr color models of images. Later, the set of features is employed on different classification frameworks, from which quadratic support vector machine (SVM) outnumbered the others with an accuracy of 98.5%. The proposed method is tested on German Traffic Sign Detection Benchmark (GTSDB) images. The results are satisfactory when compared to the preceding work.     

基于归一化RGB色彩模型的阴影处理方法

通过分析影像上阴影区域的属性，提出了一种基于阴影属性的高分辨率遥感影像阴影检测和去除算法。利用阴影区域蓝色分量偏高的特性，对归一化B分量和原始B分量进行阈值检测，并结合小区域去除和数学形态学处理，得到较精确的阴影区域；然后，分别在RGB空间和HSI空间对各个独立的阴影区域与其邻近的非阴影区域进行匹配，完成阴影去除操作；最后沿着阴影边界做一次中值滤波以减轻边缘效应。仿真结果验证了算法的有效性，并且显示在HSI空间获得了更好的补偿效果。     

The ability of color defectives to judge signal lights at sea.

Measures were made of the ability of color-defective men to judge correctly the colors of navigation lights (red, green, or white) presented to them at night under realistic sea conditions. Eighty-one color-defective men were employed; they were categorized as to type and degree of defect using a battery of five color-vision tests. While the average performance of the color-defective men was considerably poorer than that of 24 color normals, there were large individual differences within each category of defect. Attempts to account for these differences in performance by variations in acuity, intelligence, and motivation failed. The extent to which the data can be accounted for by modern color-vision theory is discussed.     

Color constancy through inverse-intensity chromaticity space

Existing color constancy methods cannot handle both uniformly colored surfaces and highly textured surfaces in a single integrated framework. Statistics-based methods require many surface colors and become error prone when there are only a few surface colors. In contrast, dichromatic-based methods can successfully handle uniformly colored surfaces but cannot be applied to highly textured surfaces, since they require precise color segmentation. We present a single integrated method to estimate illumination chromaticity from single-colored and multicolored surfaces. Unlike existing dichromatic-based methods, the proposed method requires only rough highlight regions without segmenting the colors inside them. We show that, by analyzing highlights, a direct correlation between illumination chromaticity and image chromaticity can be obtained. This correlation is clearly described in "inverse-intensity chromaticity space," a novel two-dimensional space that we introduce. In addition, when Hough transform and histogram analysis is utilized in this space, illumination chromaticity can be estimated robustly, even for a highly textured surface.     

基于数学形态学的HSI空间彩色边缘检测方法

针对在RGB空间中很难有效区分颜色相似性的问题,选择更加符合颜色视觉特性的HSI颜色空间进行图像处理,提出一种基于HSI空间的多结构多尺度自适应彩色图像边缘检测方法.首先对H、S、I三个分量采用不同结构和不同尺度的结构元素进行形态学边缘检测,然后对三个边缘分量利用信息熵加权得到融合后的彩色边缘信息.实验结果表明,该方法可以充分利用彩色图像的色度,饱和度和亮度信息,有效地抑制噪声,自适应地提取完整的边缘信息.     

Traffic sign detection and positioning based on monocular camera

ABSTRACT

Geospatial and geometric states of traffic signs are indispensable information for traffic facility maintenance. This study applies a low-cost single camera system to locate and identify traffic signs and reflects their on-site conditions for assisting in maintenance. Referring to official regulations, a traffic sign can be identified and classified based on its color and shape attributes. The poses and states of the sign planes can also be evaluated according to their geometric shape ratios and the discrepancy with respect to traffic sign regulations, in which a least-square consistency check is proposed to ensure assessment reliability and accuracy. Validation with day and nighttime image sequences was performed to verify the effectiveness of the proposed method and the robustness to illumination changes, color deterioration, and motion blur. Considering the promising results, this study can be deemed as an alternative to promote routine maintenance of traffic facilities.     

Color from shape from color: a simple formalism with known light sources

Photometric stereo is a well-known technique for recovering surface normals of a surface but requires three or more images of a surface taken under illumination from different directions. At best, one may dispense with the need for multiple images by using colored lights tuned to camera filters. But a less restrictive paradigm is available that uses the orientation-from-color approach, wherein multiple broadband illuminants impinge on a surface simultaneously. In that method, colors for a Lambertian surface lie on an ellipsoid in color space. The method has been applied mainly to single-color objects, with ellipsoid quadratic-form parameters determined from a large number of pixels. However, recently Petrov and Antonova [Color Res. Appl. 21, 97 (1996)] developed an entirely local approach, useful also for multicolored objects with color uniform in each patch. We investigate to what extent a method such as that of Petrov and Antonova can be applied in the ostensibly simpler situation in which the complex lighting environment is known, i.e., a color photometric stereo situation, with all lights in play at once with only a single image to analyze. We find that, assuming a simple model of color formation, we are able to recover the object colors along with surface normals, using only a single image. Because we immerse the object in a known lighting environment, we show that only half of the equations utilized by Petrov and Antonova are actually needed, making the method more stable. Nevertheless, solutions do not exist at every pixel; instead we may determine a best estimate of patch color, using a robust estimator, and then apply that estimate throughout a patch. Results are shown to be quite good compared with ground truth. The simple color model can often be made to hold more exactly by transforming the color space into one corresponding to spectrally sharpened sensors, which are a matrix transform away from the actual camera sensors. In our study the reliability and accuracy of the normal vector and of the surface color recovery algorithm are improved by this straightforward transformation.     

Morphological rational operator for contrast enhancement

Contrast enhancement is an important task in image processing that is commonly used as a preprocessing step to improve the images for other tasks such as segmentation. However, some methods for contrast improvement that work well in low-contrast regions affect good contrast regions as well. This occurs due to the fact that some elements may vanish. A method focused on images with different luminance conditions is introduced in the present work. The proposed method is based on morphological transformations by reconstruction and rational operations, which, altogether, allow a more accurate contrast enhancement resulting in regions that are in harmony with their environment. Furthermore, due to the properties of these morphological transformations, the creation of new elements on the image is avoided. The processing is carried out on luminance values in the u'v'Y color space, which avoids the creation of new colors. As a result of the previous considerations, the proposed method keeps the natural color appearance of the image.     

线性交通空间动态视觉公共艺术设计探究

目的探讨线性交通空间引入动态视觉公共艺术的现实意义,并提出相应的设计建议。方法分析线性交通空间的特性及公共艺术的发展情况,结合国内外相关设计案例,归纳、总结动态视觉公共艺术介入线性交通空间的设计思路和方向。结论公共艺术是城市文化传播的重要载体和窗口,在大数据和信息化的时代,线性交通应更注重乘客在旅途中的立体式感官体验;应用新媒体技术、数控技术,结合多种创新艺术表现形式的动态视觉公共艺术,符合城市公共艺术发展,改善了线性交通环境,促进了城市及地域文化交流的需求。     

A Network Traffic Classification Model Based on Metric Learning

Attacks on websites and network servers are among the most critical threats in network security. Network behavior identification is one of the most effective ways to identify malicious network intrusions. Analyzing abnormal network traffic patterns and traffic classification based on labeled network traffic data are among the most effective approaches for network behavior identification. Traditional methods for network traffic classification utilize algorithms such as Naive Bayes, Decision Tree and XGBoost. However, network traffic classification, which is required for network behavior identification, generally suffers from the problem of low accuracy even with the recently proposed deep learning models. To improve network traffic classification accuracy thus improving network intrusion detection rate, this paper proposes a new network traffic classification model, called ArcMargin, which incorporates metric learning into a convolutional neural network (CNN) to make the CNN model more discriminative. ArcMargin maps network traffic samples from the same category more closely while samples from different categories are mapped as far apart as possible. The metric learning regularization feature is called additive angular margin loss, and it is embedded in the object function of traditional CNN models. The proposed ArcMargin model is validated with three datasets and is compared with several other related algorithms. According to a set of classification indicators, the ArcMargin model is proofed to have better performances in both network traffic classification tasks and open-set tasks. Moreover, in open-set tasks, the ArcMargin model can cluster unknown data classes that do not exist in the previous training dataset.     

基于车流密度的道路噪声源动态辐射模型

相较于车流相对稳定的高速公路而言,城市交通流因上下班高峰、红绿灯、交叉路口等影响,具有明显的波动性。现有噪声预测模式中,声源辐射模型采用小时流量及设计速度作为独立变量,车速与车流量没有关联,仅适用于测量常年平均声级,不能正确反映城市道路噪声的动态变化特征。文章对交通流模型进行了调查,以行车速度与车流密度制约关系为基础,结合道路车辆的物理模型及声源辐射模型,建立以车流密度为变量的噪声源动态声源辐射计算模型,描述城市道路交通噪声辐射声级随车流密度的动态变化特征,与已有模型进行对比并通过实测,验证了新模型能更好地反映城市道路车辆声源辐射声级的变化情况。     

基于大数据的配色方案优化约束空间建构

目的 从图像中提取的特征色通常表达为一系列单色,即基于聚类法得到的聚类中心。在以图像色彩意象再现为目标的配色方案优化中,很少考虑提取色的聚类分散度,导致配色方案的色彩过于集中在聚类中心而失去了多样性。基于聚类结果为优化过程提供更精准有效的约束。方法 首先用K–Means聚类方法从图库的每一幅图中提取若干特征色彩,并集中表达在可视化的三维色彩空间中;然后用户通过交互方式人工选择色彩构筑一系列定制色彩空间,作为色彩变化的约束条件,即约束空间;并设计了基于约束空间的配色优化交互式遗传算法。结果 以非遗图库的色彩意象再现为目标,基于平面设计的色彩优化任务进行了测试,在效率和满意度两个方面均表现良好。结论 解决了提取色重用的约束条件施加方法问题,有效利用了提取色的分布信息,使优化过程可以更精准地再现原始图库的意象,特别是色彩的分布特征。     

基于PLC的十字路口交通信号灯控制

随着社会的进步、科技的发展,编程控制器应用的越来越广泛,冶金、石油、化工、建材、机械制造、电力、汽车、轻工、环保及文化娱乐等各行各业都能看到可编程控制器的身影。将可编程控制器运用到城市十字路口交通信号灯上,使我们的城市交通运行的更有规律。