The motion analysis of fire video images based on moment features and flicker frequency

In this paper, motion analysis methods based on the moment features and flicker frequency features for early fire flame from ordinary CCD video camera were proposed, and in order to describe the changing of flame and disturbance of non-flame phenomena further more, the average changing pixel number of the first-order moments of consecutive flames has been defined in the moment analysis as well. The first-order moments of all kinds of flames used in our experiments present irregularly flickering, and their average changing pixel numbers of first-order moments are greater than fire-like disturbances. For the analysis of flicker frequency of flame, which is extracted and calculated in spatial domain, and therefore it is computational simple and fast. The method of extracting flicker frequency from video images is not affected by the catalogues of combustion material and distance. In experiments, we adopted two kinds of flames, i. e. , fixed flame and movable flame. Many comparing and disturbing experiments were done and verified that the methods can be used as criteria for early fire detection.

Image Zernike Moments Shape Feature Evaluation Based on Image Reconstruction

The evaluation approach to the accuracy of the image feature descriptors plays an important role in image feature extraction. We point out that the image shape feature can be described by the Zernike moments set while briefly introducing the basic concept of the Zernike moment. After talking about the image reconstruction technique based on the inverse transformation of Zernike moment, the evaluation approach to the accuracy of the Zernike moments shape feature via the dissimilarity degree and the reconstruction ratio between the original image and the reconstructed image is proposed. The experiment results demonstrate the feasibility of this evaluation approach to image Zernike moments shape feature.

Evolutionary Computation Based Optimization of Image Zernike Moments Shape Feature Vector

The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. So the optimization algorithm based on evolutionary computation is designed and implemented in this paper to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.

AUTOMATIC TARGET RECOGNITION OF RADAR HRRP BASED ON HIGH ORDER CENTRAL MOMENTS FEATURES

The paper addresses the problem of target recognition using High-resolution Radar Range Profiles(HRRP).A novel approach of feature extraction and dimension reduction based on extended high order central moments is proposed in order to reduce the dimension of range profiles.Features extracted from radar HRRPs are normalized and smoothed,and then comparative analysis of the similar approaches is done.The range profiles are obtained by step frequency technique using the two-dimensional backscatters distribution data of four different aircraft models.The template matching method by nearest neighbor rules,which is based on the theory of kernel methods for pattern analysis,is used to classify and identify the range profiles from four different aircrafts.Numerical simulation results show that the proposed approach can achieve good performance of stability,shift independence and higher recognition rate.It is helpful for real-time identification and the engineering implements of automatic target recognition using HRRP.The number of required templates could be reduced con-siderably while maintaining an equivalent recognition rate.

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.

PRODUCT IMAGE RETRIEVAL BASED ON CO-FEATURES OF THE OBJECT

In this paper, we propose a product image retrieval method based on the object contour corners, image texture and color. The product image mainly highlights the object and its background is very simple. According to these characteristics, we represent the object using its contour, and detect the corners of contour to reduce the number of pixels. Every corner is described using its approximate curvature based on distance. In addition, the Block Difference of Inverse Probabilities (BDIP) and Block Variation of Local Correlation (BVLC) texture features and color moment are extracted from image's HIS color space. Finally, dynamic time warping method is used to match features with different length. In order to demonstrate the effect of the proposed method, we carry out experiments in Mi-crosoft product image database, and compare it with other feature descriptors. The retrieval precision and recall curves show that our method is feasible.

论Moment in Peking汉译本的无本回译现象

以林语堂原创小说Moment in Peking无本回译为研究对象,基于英汉双语平行语料库,立足文化调适视角,考察分析了Moment in Peking无本回译的语言特征和翻译策略选择。结果显示:在词汇层面,无本回译作品的整体用词比原作丰富多样;在句法层面,无本回译作品表现出句法简略化特征;在语篇层面,无本回译作品以原作为依归,注重小说主题复现;在副文本层面,无本回译作品颇具创造性,为本位文化读者解读译者的翻译思想和主体性发挥提供助力。在无本回译策略选择上,同化和整合策略指导下的无本回译倾向归化翻译;分离策略指导下的无本回译倾向异化翻译,边缘化策略体现不明显。

Performance of Object Classification Using Zernike Moment

Moments have been used in all sorts of object classification systems based on image. There are lots of moments studied by many researchers in the area of object classification and one of the most preference moments is the Zernike moment. In this paper, the performance of object classification using the Zernike moment has been explored. The classifier based on neural networks has been used in this study. The results indicate the best performance in identifying the aggregate is at 91.4% with a ten orders of the Zernike moment. This encouraging result has shown that the Zernike moment is a suitable moment to be used as a feature of object classification systems.

Multi-level spherical moments based 3D model retrieval

In this paper a novel 3D model retrieval method that employs multi-level spherical moment analysis and relies on voxelization and spherical mapping of the 3D models is proposed. For a given polygon-soup 3D model, first a pose normalization step is done to align the model into a canonical coordinate frame so as to define the shape representation with respect to this orientation. Afterward we rasterize its exterior surface into cubical voxel grids, then a series of homocentric spheres with their center superposing the center of the voxel grids cut the voxel grids into several spherical images. Finally moments belonging to each sphere are computed and the moments of all spheres constitute the descriptor of the model. Experiments showed that Euclidean distance based on this kind of feature vector can distinguish different 3D models well and that the 3D model retrieval system based on this arithmetic yields satisfactory performance.

A Simple Computational Approach for the Texture Analysis of CT Scan Images Using Orthogonal Moments

This paper is a study on texture analysis of Computer Tomography (CT) liver images using orthogonal moment features. Orthogonal moments are used as image feature representation in many applications like invariant pattern recognition of images. Orthogonal moments are proposed here for the diagnosis of any abnormalities on the CT images. The objective of the proposed work is to carry out the comparative study of the performance of orthogonal moments like Zernike, Racah and Legendre moments for the detection of abnormal tissue on CT liver images. The Region of Interest (ROI) based segmentation and watershed segmentation are applied to the input image and the features are extracted with the orthogonal moments and analyses are made with the combination of orthogonal moment with segmentation that provides better accuracy while detecting the tumor. This computational model is tested with many inputs and the performance of the orthogonal moments with segmentation for the texture analysis of CT scan images is computed and compared.

基于几何矩特征与纹理度量的图像匹配算法

为提高多种几何内容变化下的特征点匹配准确度,引入双边滤波器,设计了几何矩特征耦合纹理度量的图像匹配算法。引入双边滤波器,对图像进行预处理,以去除图像中噪声,接着利用Hessian算子,准确检测图像特征。利用图像灰度信息,计算图像特征圆域内的几何矩,以形成鲁棒性较强的特征向量。通过平均梯度模型,对特征点邻域的纹理特征进行度量,并将其联合欧氏测量结果,对图像特征实施匹配。借助互相关模型,对匹配特征的相关程度进行测量,以检测特征点的匹配正确性,优化特征匹配结果。实验数据表明:较当前特征匹配方法而言,在多种几何内容变化下,所提技术具有更高的匹配准确度与鲁棒性。

顾及加权Sobel滤波特征增强的空天影像稳健匹配方法

针对航空航天影像之间存在显著的跨视角差异、尺度和尺寸差异、旋转和位移差异以及非线性辐射差异,导致空天影像匹配难以高效稳定的问题,设计了一种顾及加权Sobel滤波特征增强的空天影像稳健匹配方法,从而完成卫星影像和无人机影像之间的稳健匹配。首先,对空天影像进行相位一致性计算,得到相位最大最小力矩图,并对力矩图进行特征点检测;然后,使用顾及加权Sobel滤波特征增强的加权梯度特征描述子对特征点进行描述;最后,利用欧氏距离作为匹配测度进行同名点识别,将多组存在不同程度的视角差异、旋转差异、尺度差异和非线性辐射差异的空天影像作为数据源,分别与基于改进SURF检测器的局部高分辨率图像配准、定向自相似直方图匹配、位置尺度定向不变特征变换和尺度不变特征变换等算法进行了对比实验。结果表明:所提方法在综合匹配性能上明显优于其他方法,能够实现航空航天影像的稳健匹配。

基于不变矩卡尔曼预测的核相关跟踪算法改进

针对KCF(核相关滤波器)算法在目标发生严重遮挡时易出现目标丢失的问题,提出了一种结合不变距特征及卡尔曼预测的核相关滤波跟踪算法。利用模板图像和当前帧图像的不变矩特征计算相关量,然后根据不同遮挡情况下相关量值的变化情况设定目标遮挡判断机制。当目标未遮挡时KCF算法对目标继续跟踪;当目标被遮挡时利用卡尔曼滤波预测目标位置,目标复现后KCF算法利用预测的位置信息继续跟踪。最后采用OTB-2015数据集测试算法的有效性,实验结果表明,论文算法相对于KCF算法跟踪精度和跟踪成功率分别提高了12.95%和13.28%,有效改善了遮挡情况下的目标跟踪性能。

基于加权多矩融合特征的矿物影像智能识别算法研究

随着数字识别技术在镜下影像分析的广泛应用,镜下物质类型的智能识别成为了一个微观分析的基础问题。镜下影像自动识别不仅能有效节约人工成本,还能提高识别效率。针对镜下矿物智能识别精度低的问题,以镜下影像的颜色矩、纹理矩以及形态RSTC矩3类指标为识别特征,以指标熵权和变异系数权为识别初始权,构建了一种多矩融合机器学习智能识别模型。选取磁铁矿、云母、方解石、黄铜等的影像集为第一类样本,以烧结矿中的玻璃相、铁酸钙等影像作为第二类样本,提取样本颜色矩、纹理矩和形状RSTC矩的特征,量化分析了特征在影像识别中的贡献率,开展了多矩融合机器学习智能识别试验。结果表明:不同类型特征指标对影像识别过程贡献率有明显差异,多矩融合机器学习智能识别模型具有较好的识别率和鲁棒性,能够明显提高影像识别精度,指标熵权和变异系数权为初始权能够明显促进算法快速收敛,减少识别时间,该研究为矿石镜下影像识别提供了新的方法。

基于多维矩特征联合的K分布杂波参数估计

K分布杂波参数估计在海洋雷达自适应目标检测中起着关键作用。传统的矩估计器通过联立两个矩求解参数,其估计性能受限于有限矩的信息。因此,本文提出一种基于多维矩特征联合的参数估计方法,旨在拓展矩信息的维度。首先,从观测数据中,提取多个精心设计的线性矩和对数矩,构建一个特征向量。其次,将传统基于统计分布的参数估计问题转换为非线性优化问题。然后,通过引入梯度提升决策树(Gradient Boosting Decision Tree, GBDT)算法,建立特征向量和形状参数之间的函数关系,实现形状参数的估计。此外,推导证明特征向量与尺度参数的独立性以及二阶矩只依赖于尺度参数,从而解决两个参数估计的相关性问题。最后,仿真和实测数据结果表明,所提估计器能利用多个矩的丰富信息,进一步提高参数估计性能。特别是在小形状参数时,其估计性能显著优于现有矩估计法和zrlog(z)期望法。

结合四元数拉盖尔矩和三维结构的图像哈希

目前图像哈希领域中许多算法只能处理灰度图像,为扩大算法适用范围,同时提高图像哈希算法的性能与旋转攻击的鲁棒性,提出基于四元数拉盖尔矩和三维能量结构的哈希算法。首先对输入的彩色图像进行预处理与多尺度融合处理,将融合图像提取的拉盖尔矩系数作为图像的全局特征,同时在YCb Cr颜色空间中利用融合图像的能量信息建立模型,选取三维模型中不同视角下的能量峰值和谷值点连线与水平面的夹角作为局部结构特征;然后根据特定点与三维模型各条等高线上近点和远点的位置提取具有旋转不变性的特征;最后结合全局特征和三维结构特征,量化并加密生成哈希序列。实验结果表明,该算法在鲁棒性与区别性之间有更好的平衡,受试者工作特征曲线错误接受率为0时的正确接受率达到0.9992。当哈希序列长度为120 bit时具备最优的紧凑性,平均计算时间为0.0979 s。在拷贝检测实验中,该算法进行多次抽取实验的平均查全率和查准率均在95.83%以上。

基于遗传算法优化支持向量机的船舰目标识别分类

为了实现有效的海上监管和响应,提高舰船监管效率,降低人力成本,提出基于遗传算法优化支持向量机的舰船目标识别分类方法。以HU矩为舰船目标的特征描述子,在舰船目标图像内,提取具备旋转、尺度与平移不变性的舰船目标特征矩;利用遗传算法,优化支持向量机的惩罚因子与核参数;在参数优化后的支持向量机内,输入舰船目标特征矩样本,输出舰船目标识别分类结果。实验证明,该方法可有效提取舰船目标特征矩;经过参数优化后的支持向量机,可有效降低计算复杂度,加快检测目标识别分类效率,具备较优的舰船目标识别分类性能。该方法均可精准识别分类舰船目标。

Identification of ultra-high-frequency PD signals in gas-insulated switchgear based on moment features considering electromagnetic mode

The feature extraction and pattern recognition techniques are of great importance to assess the insulation condition of gas-insulated switchgear.In this work,the ultra-high-frequency partial discharge(PD)signals generated from four types of typical insulation defects are analysed using S-transform,and the greyscale image in time-frequency representation is divided into five regions according to the cutoff frequencies of TEm1 modes.Then,the three low-order moments of every subregion are extracted and the feature selection is performed based on the J criterion.To confirm the effectiveness of selected moment features after considering the electromagnetic modes,the support vector machine,k-nearest neighbour and particle swarm-optimised extreme learning machine(ELM)are utilised to classify the type of PD,and they achieve the recognition accuracies of 92,88.5 and 95%,respectively.In addition,the results show that the ELM offers good generalisation performance at the fastest learning and testing speeds,thus more suitable for a real-time PD detection.

CADCAM开发平台及其发展趋势

基于CADCAM平台的系统开发是一种重要的开发方式,研究平台对CADCAM系统的开发有重要意义.介绍了三种主流CADCAM系统开发平台ACIS,CAS.CADE和PARASOLID,阐述了平台的主要功能、开发接口和数据结构,并分析其各自特色,最后总结了CADCAM平台的发展趋势.

基于多特征的杂草逆向定位方法与试验

提出了一种基于多特征的杂草逆向定位方法。以田间作物作为研究对象,将多目标杂草定位问题转换为单目标的作物定位问题。采用作物叶片HU不变矩与形状特征的识别准确定位出每一株作物,然后基于颜色特征将作物区域以外的绿色植物均认定为杂草。设计了一款小型杂草定位装置,并应用在宽幅喷药机上。田间试验结果表明,在喷药机工作速度为5 km/h时,该系统对于大豆田间杂草识别的准确率为90%以上,较好地解决了杂草定位与精细喷洒农药问题。