RGB到CIEXYZ色彩空间转换的研究

介绍了RGB和CIEXYZ颜色空间,采用多项式回归算法建立了RGB到CIEXYZ色彩转换模型,并编程实现了转换实例,最后分析了此模型的精度.     

基于划分子空间的数码相机颜色空间转换方法研究

基于数码相机的印刷品质量检测是未来发展的方向,在颜色检测领域需要首先解决色空间转换的精度问题。采用分子空间的多项式回归法实现了从RGB 颜色空间到CIEL* a* b* 颜色空间的转换,首先把RGB 颜色空间划分成若干个子空间,然后在每个子空间中运用最小二乘法建立多项式模型,对任意RGB 颜色值根据其所在子空间求解对应的多项式方程,即可得到L*a*b*值。实验表明,该方法的转换精度有了很大程度的提高,能够满足数码相机色空间转换的基本要求,为基于数码相机的印刷品质量检测奠定基础。     

印刷品质量检测颜色转换模型研究

目的研究印刷品质量检测中的颜色转换模型。方法基于色靶的测量数据,采用三维查找表法和多项式回归法,分别建立RGB与CIE L*a*b*颜色空间转换模型,并实验比较了2种算法转换模型的精度。结果三维查找表法转换模型的最大色差为3,四面体插值算法精度稍高于三线性、三棱柱和金字塔插值算法,平均色差为0.64;多项式回归算法的精度随着项数的增多而提高,20项的平均色差为2.58。结论三维查找表法转换模型精度高于多项式回归法,效果理想,能判断出CCD采集到的印刷图像是否存在色偏,且随着RGB颜色空间划分越细小,转换精度会越高。     

基于立方体内缩的反向颜色空间转换算法

目的提出一种基于立方体内缩的反向颜色空间转换算法,实现颜色值从CIELab颜色空间到RGB颜色空间的转换。方法选取4096组建模点和512组测试点,以三维查表插值法为理论基础,设计一种基于立方体内缩的方法逐步搜索符合条件的特征点来代替测试点,计算得到测试点反向转换后的RGB值。结果通过CIELAB 1976,CIE94和CIE2000色差公式对算法的转换精度进行评价,计算测试点的Lab值和反向转换得到RGB值对应的Lab值之间的色差。测试点的平均色差分别为2.07,1.53和0.96,大部分色差的分布范围在0~3之间,算法的转换精度较高。测试点反向转换后的结果较为理想,在绿色调区域和亮度较低的红色调区域的精度可进一步提升。结论实验结果表明此算法可精准、快速、有效地实现颜色值从CIELab颜色空间到RGB颜色空间的转换。     

基于多项式回归模型的显示器特征化研究

在用色彩管理软件以及分光光度仪对显示器进行屏幕的校准和特性化后,以多项式回归算法建立RGB到Lab 颜色转换模型,通过实验分析了多项式回归模型不同项数、不同样本对液晶显示器特性化精度的影响。结果显示,用多项式回归算法建立的三阶三元二十项多项式可以得到色差为2. 5 左右精度较高的预测模型。     

基于BP神经网络的数码相机颜色特征化

以色度学理论和神经网络为基础,在综合比较数码相机颜色特征化方法之后,采用了基于BP神经网络的颜色特征化方法。利用MATLAB实现了从RGB到XYZ及Lab的颜色空间转换,比较了2种转换模型的精度,统计分析了该模型产生训练误差和测试误差的主要原因,验证了RGB→Lab转换算法是一种较优和可靠的方法。     

设备特征化过程中的三棱柱插值与多项式回归算法研究

介绍了目前设备特征化过程中色彩空间转换的主要方法,以从RGB色空间到Lab色空间的转换为例,讨论了三棱柱插值和多项式回归这2种算法,并通过实验检验了2种算法的效果,对实验数据进行了对比分析。结果表明,这2种算法在色彩管理领域的应用均有继续改进的空间。     

基于三维查找表的RGB到XYZ颜色空间转换的研究

在分析三维查找表算法基本原理的基础上,采用Matlab编程建立了RGB到XYZ颜色空间的转换模型,并实验比较了4种插值算法的模型精度。研究结果表明:转换模型的转换精度较高,效果理想,且随着RGB颜色空间被划分的越细小,转换精度会越高。     

BP 神经网络在显示器色空间转换中的应用

用色彩管理软件以及分光光度仪对显示器的屏幕进行了校准和特性化,采用BP 神经网络法建立了从RGB 色空间到Lab 色空间的转换模型。通过对实验数据进行对比分析,结果表明这种算法对色彩空间转换具有较好的非线性拟合能力和更高的预测准确性。     

基于颜色空间转换的颜色复原方法研究

目的 研究解决因成像原理、 元件性能、 机械上的限制等因素导致的色彩失真与偏差的方法。方法 通过对基于BP神经网络的颜色复原和基于全局多项式回归的颜色复原等2种方法进行对比研究, 提出基于色调分区多项式回归的、 由 RGB 到 L*a*b*的颜色复原转换方法。结果 基于 BP 神经网络的颜色复原得到的最小色差为 2.8476, 基于全局多项式回归的颜色复原得到的最小色差为2.857, 二者相差仅 0.3%; 而经过分区后的多项式回归颜色复原得到的平均色差为 2.206, 比基于 BP神经网络和全局多项式回归方法降低了 23%左右的色差。结论 经过分区后的多项式回归颜色复原方法能更有效地提高颜色复原的精度。     

Visible imaging characteristics of the space target based on bidirectional reflection distribution function

A modelling method for visible imaging characteristics of a space target is presented. Background radiation of a space target consists of direct solar radiation, reflected radiation from the earth and the moon, and that from other stars. The target surface was divided into grids and the light reflection properties of each grid are described by introducing a bidirectional reflection distribution function (BRDF) model obtained in advance. Then a mathematical model for the visible imaging properties of the space target was built using given parameters of the optical detection system. Visual surfaces of the target to detection system were determined by a vector coordinate method. Simulation of the target optical imaging characteristics in orbit was achieved according to its given physical dimensions and parameters. The results show the method is feasible and robust for optical characteristics of the space target. It can provide a facility for real-time analysis of optical imaging characteristics of space targets.     

Anomaly IoT Node Detection Based on Local Outlier Factor and Time Series

The heterogeneous nodes in the Internet of Things (IoT) are relatively weak in the computing power and storage capacity. Therefore, traditional algorithms of network security are not suitable for the IoT. Once these nodes alternate between normal behavior and anomaly behavior, it is difficult to identify and isolate them by the network system in a short time, thus the data transmission accuracy and the integrity of the network function will be affected negatively. Based on the characteristics of IoT, a lightweight local outlier factor detection method is used for node detection. In order to further determine whether the nodes are an anomaly or not, the varying behavior of those nodes in terms of time is considered in this research, and a time series method is used to make the system respond to the randomness and selectiveness of anomaly behavior nodes effectively in a short period of time. Simulation results show that the proposed method can improve the accuracy of the data transmitted by the network and achieve better performance.     

Network Security Situation Awareness Framework based on Threat Intelligence

Network security situation awareness is an important foundation for network security management, which presents the target system security status by analyzing existing or potential cyber threats in the target system. In network offense and defense, the network security state of the target system will be affected by both offensive and defensive strategies. According to this feature, this paper proposes a network security situation awareness method using stochastic game in cloud computing environment, uses the utility of both sides of the game to quantify the network security situation value. This method analyzes the nodes based on the network security state of the target virtual machine and uses the virtual machine introspection mechanism to obtain the impact of network attacks on the target virtual machine, then dynamically evaluates the network security situation of the cloud environment based on the game process of both attack and defense. In attack prediction, cyber threat intelligence is used as an important basis for potential threat analysis. Cyber threat intelligence that is applicable to the current security state is screened through the system hierarchy fuzzy optimization method, and the potential threat of the target system is analyzed using the cyber threat intelligence obtained through screening. If there is no applicable cyber threat intelligence, using the Nash equilibrium to make predictions for the attack behavior. The experimental results show that the network security situation awareness method proposed in this paper can accurately reflect the changes in the network security situation and make predictions on the attack behavior.     

基于逻辑回归模型的流量异常检测方法研究

网络流量作为异常检测的基本数据源,其行为特征的准确描述,是网络异常行为实时检测的重要依据.本文针对流量异常检测问题,提出了一种基于逻辑回归模型的网络流量异常检测方法.通过分析源IP、目的 IP等多个网络流量基本特征,构造了网络异常行为和正常行为的训练机,并且在此基础上采用逻辑回归建立起网络异常流量挖掘模型.利用实验室所采集的真实网络流量对所构建的模型进行检测,以验证该模型的有效性.实验结果表明本文所建立的网络模型在异常流量挖掘方面准确度高、实时性好.     

Performance Anomaly Detection in Web Services: An RNN- Based Approach Using Dynamic Quality of Service Features

Performance anomaly detection is the process of identifying occurrences that do not conform to expected behavior or correlate with other incidents or events in time series data. Anomaly detection has been applied to areas such as fraud detection, intrusion detection systems, and network systems. In this paper, we propose an anomaly detection framework that uses dynamic features of quality of service that are collected in a simulated setup. Three variants of recurrent neural networks-SimpleRNN, long short term memory, and gated recurrent unit are evaluated. The results reveal that the proposed method effectively detects anomalies in web services with high accuracy. The performance of the proposed anomaly detection framework is superior to that of existing approaches using maximum accuracy and detection rate metrics.     

基于弱磁技术的火车轮踏面裂纹检测

针对火车轮踏面易萌生细小裂纹的问题,提出一种无需励磁,利用地磁场磁化的无损检测方法.根据火车轮踏面结构设计探头工装,利用弱磁检测仪器采集车轮踏面磁感应强度信号,分析预制裂纹缺陷的磁异常信号特征,对比不同深度裂纹的磁异常信号幅值,通过傅里叶最小二乘拟合得到缺陷深度的定量算法,根据极值差法与拉依达准则对火车轮踏面进行智能识...     

一种基于色彩处理的被动声呐宽带目标检测显示方法

针对被动声呐传统宽带能量检测方式难以解决强干扰存在时的弱目标检测问题,着眼于目标、干扰及背景噪声频谱特征差异,提出了一种划分子带,利用颜色合成理论进行宽带融合检测的显示方法。针对弱目标存在稳定线谱或在部分频点能量相对较强的情况,可有效提升对弱目标的检测能力,并增强不同目标方位历程的区分度。     

基于RBF神经网络的色空间转换模型

研究了RBF神经网络的结构及算法,应用RBF神经网络建立了打印机的色空间转换模型.根据实验数据,对网络结构进行了优化,通过比较不同参数时网络的性能,确定最优网络参数.最后对所建模型进行了仿真验证,验证结果表明,预测数据与实测数据的色差较小,说明该模型具有实用价值.