融合式空间塔式算子和HIK-SVM的交通标志识别研究

在交通标志识别问题上,提出了一种基于融合式的空间塔式算子和直方图交叉核支持向量机(HIK-SVM)的分类方法.在该方法中,通过提取图像的灰度塔式词袋直方图(Gray-PHOW)特征、颜色塔式词袋直方图(Color-PHOW)特征和塔式边缘方向梯度直方图(PHOG)特征来对交通标志的外观、颜色和轮廓信息进行描述.通过提取空间塔式直方图特征,能很好地对图像各种特征的空间分布状况进行描述.提取到图像的外观、颜色、轮廓和特征的空间分布信息后,对其进行融合,最后得到的融合式的空间塔式特征具有很强的鲁棒性.将该融合式特征送入HIK-SVM进行训练和分类,取得了极其高的识别效果.     

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为了提高复杂环境下智能视频监控对运动目标跟踪的可靠性,提出一种基于改进的颜色特征和直方图更新的Mean-Shift跟踪算法.该算法利用像素点局部颜色变化作为跟踪目标像素点特征,弥补单一颜色值特征对目标表征的不足;并根据Mean-Shift跟踪结果和目标匹配程度对目标直方图进行更新,保证在目标姿态和大小发生变化时目标特征直方图的有效性.实验结果表明：与传统Mean-Shift算法相比,改进的颜色特征减小了相似背景像素对运动目标的干扰,目标直方图的更新提高了Mean-Shift算法对目标姿态和大小改变的鲁棒性.算法提高了基于颜色特征的Mean-Shift算法对复杂环境中运动目标进行实时跟踪的可靠性.     

Range profile target recognition using sparse representation based on feature space

A novel method is presented to improve the recognition rate of warhead in this paper. Firstly, a tool for electromagnetic calculation, like CST Microwave Studio, is used to simulate the frequency response of the electromagnetic scattering. Secondly, the echo and further the range profile are acquired from the frequency response by further processing. Thirdly, a set of discriminative features is extracted from the range profiles of the target. Fourthly, these features are used to construct a dictionary for the sparse representation classifier. Finally, the sample of the target can be classified by solving the sparsest coefficients. Since the reconstruction result is determined by a linear combination of the training samples, this method has a good robustness for the variable features. By formulating the problem within a feature-based sparse representation framework, the presented method combines the discriminative features of each sample during the sparse recovery process rather than in a postprocessing manner. Moreover, based on the feature representation space rather than a single feature or image pixel, the constructed dictionary exhibits both strong expressive and discriminative powers that can enhance the classification performance of the test sample. A series of test results based on the simulated data demonstrates the effectiveness of our method.     

基于边权重图神经网络的一阶逻辑前提选择

为提高自动定理证明器在大规模问题中证明问题的能力，前提选择任务应运而生. 由于公式图的有向性，主流的图神经网络框架只能单向地对节点进行更新，且无法编码公式图中子节点间的顺序. 针对以上问题，提出了带有边类型的双向公式图表示方法，并提出了一种基于边权重的图神经网络（edge-weight-based graph neural network，EW-GNN）模型用于编码一阶逻辑公式. 该模型首先利用相连节点的信息来更新对应边类型的特征表示，随后利用更新后的边类型特征计算邻接节点对中心节点的权重，最后利用邻接节点的信息双向地对中心节点进行更新. 实验比较分析表明:基于边权重的图神经网络模型在前提选择任务中表现得更加优越，其在相同的测试集上比当前最优模型的分类准确率高了约1%.      

Unsupervised color-texture image segmentation

The measure J in J value segmentation （JSEG） fails to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An improved approach for JSEG algorithm was proposed for unsupervised color-texture image segmentation. The texture and photometric invariant edge information were combined, which results in a discriminative measure for color-texture homogeneity. Based on the image whose pixel values are values of the new measure, region growing-merging algorithm used in JSEG was then employed to segment the image. Finally, experiments on a variety of real color images demonstrate performance improvement due to the proposed method.     

Weak correlation dictionary construction method for sparse coding

For sparse coding, the weaker the correlation of dictionary atoms is, the better the representation capacity of dictionary will be. A weak correlation dictionary construction method for sparse coding has been proposed in this paper. Firstly, a new dictionary atom initialization is proposed in which data samples with weak correlation are selected as the initial dictionary atoms in order to effectively reduce the correlation among them. Then, in the process of dictionary learning, the correlation between atoms has been measured by correlation coefficient, and strong correlation atoms have been eliminated and replaced by weak correlation atoms in order to improve the representation capacity of the dictionary. An image classification scheme has been achieved by applying the weak correlation dictionary construction method proposed in this paper. Experimental results show that, the proposed method averagely improves image classification accuracy by more than 2%, compared to sparse coding spatial pyramid matching (ScSPM) and other existing methods for image classification on the datasets of Caltech-101, Scene-15, etc.     

基于灰色理论的产品配色意象值评价算法研究

顺应产品造型变化的配色调查有助于设计师获得较为精确的配色意象,为了节省大量的配色问卷调查统计时间,以较少的已知的色彩样本意象评估量值去预测待测的产品配色量值,文中建立了产品配色方案灰色关联的数学模型,提出配色方案灰色聚类分析的算法,并详细阐述了该算法对配色方案进行定量评价的过程,最后通过案例应用和基于类神经网络预测的方法进行比较研究,结果显示,文章提出的算法对于多色组成的产品较为有效.     

基于图像融合和DCT域增强的人脸识别

提出了一种基于图像融合和DCT域增强的人脸识别方法.首先对人脸图像分别进行直方图均衡化和对指数变换,将二者变换结果融合,减少光照对人脸识别的影响;然后进行DCI变换,在滤除高频分量的同时进行图像增强,抑制块效应,在此基础上进行IDCT图像重建,利用二维主成分分析提取人脸特征,降低空间维数;最后通过最邻近分类法实现人脸识别.Yale人脸库仿真实验表明,该方法在光照变化较大和人脸样本较少的情况下具有较高的识别率.     

基于GPGPU的快速医学图像FCM分割算法

由于医学图像生成容易受到空间时间影响,噪声较大,具有不确定性,传统的硬分割方法很难取得理想的分割结果.模糊分类技术能很好地处理医学图像中的不确定性,却由于计算量大不能保证实时性.灰度统计方法和通用计算图形处理器技术的引入,保证了初始聚类中心的准确性.又由于模糊C均值聚类算法是可并行的,将其改进并在图形处理器上完成计算,降低了算法迭代次数和计算时间,保证了实时性.实验结果表明,使用该方法对医学图像分割得到了良好的结果.     

复杂环境基于多信息融合的车辆跟踪方法

平交路口复杂环境下基于视觉的车辆跟踪容易受到如车辆在图像上投影的尺 度变化,车辆的排队与消散过程中邻近车辆间的遮挡及分离等因素的影响.针对该问题, 本文提出了一种利用局部特征增强的Mean-shift 改进算法,利用SIFT 特征点对尺度、旋 转变化鲁棒的特性,将其与基于跟踪区域颜色特征的跟踪方法相融合实现车辆跟踪,较 好地解决了在车辆尺度、运动方向变化,以及遮挡情况下的跟踪问题.同时通过引入跟踪 车辆分离的判定条件,结合特征点聚类算法解决了相邻车辆发生分离时的判断及跟踪问 题.实验结果表明,在多种交通场景的车辆跟踪过程中,本文提出的算法有较好的鲁棒性, 定位结果更加精确.     

基于TQWT和稀疏表示的滚动轴承故障诊断方法

基于稀疏表示理论,提出了一种采用可调品质因子小波变换(TQWT)的滚动轴承故障诊断新方法,分析了包含早期故障成分的原始采集振动信号的特点和早期故障信号的特性,研究了稀疏表示模型在解决故障特征提取问题和故障类型识别问题的应用;运用TQWT将原始信号转换为一组子带小波系数集,研究了利用迭代收缩阈值算法提取出稀疏小波系数的有效性和谱峭度对故障冲击信号敏感的特性,通过计算各子带信号分量的谱峭度,选取包含故障信息明显的子带小波系数,建立了包含稀疏故障信号分量的故障特征提取方法;利用提取出的故障信号稀疏表示分类模型,实现了基于稀疏表示的滚动轴承故障诊断方法。试验结果表明:在凯斯西储数据集上,提出的故障特征提取方法在剔除干扰成分方面有显著效果,提出方法对于4种类型数据的平均诊断准确率为99.83%,对于10种类型数据的平均诊断准确率为97.73%;与只运用TQWT和迭代收缩阈值算法进行故障特征提取的方法相比,故障诊断精度提高了11.60%,算法运行时间减小8%;在QPZZ-Ⅱ旋转机械平台采集到的振动数据集上,提出的方法对于4种类型数据的平均诊断准确率为100%;与传统小波去噪方法相比,准确率提高了35.67%,算法运行时间减小了7.25%。可见,本文提出的方法可以有效解决滚动轴承故障诊断问题。      

基于结构性字典学习的高光谱遥感图像分类

为提高高光谱遥感图像的分类精度,提出了一种新的结构性稀疏表示及字典学习的高光谱遥感图像分类方法.该方法能同时利用高光谱遥感图像像素间的空间及光谱关系得到表示每个像素的字典,被划分为同一像素组的像素具有通用的稀疏模式;由字典计算图像的稀疏表示系数获得遥感图像的稀疏表示特征;利用线性支持向量机算法实现对高光谱遥感图像的分类.对AVIRIS和ROSIS高光谱遥感图像进行的实验结果表明:提出的方法比普通字典学习分类精度分别提高0.041 1和0.046 6,Kappa系数分别提高0.179 3和0.056 3.      

基于改进的均值漂移的森林火灾图像提取技术

为了解决呈红色山地路面等对火灾图像提取的干扰问题,考虑林区图像的R通道直方图的分布曲线,采用一种基于均值漂移改进算法,自适应地得到背景与火灾,山地路面的分割闽值T1.通过阈值分割将火灾和山地路面从背景图像中分割出来,再利用改进算法对R通道占比率直方图进行处理,自适应地得到火灾与山地路面的分割阈值T2,从而实现火灾的自适...     

COLOR IMAGE QUANTIZATION WITH EVOLUTIONARY PROGRAMMING

Introduction　　 The need for quantization of color images isarised because of the limitations of image displayand hardcopy,data storage and data transmissiondevices.The color image quantization is a complexdata clustering problem due to the broad distribu-tion of local optima in the three- dimensional colorspace. Many of the present algorithms for colorquantization find non- optimal solutions,giving riseto visible shifts in color and false contours whenthe number of quantization colors is sma…     

Support vector regression based color image restoration in YUV color space

A support vector regression(SVR) based color image restoration algorithm is proposed.The test color images are firstly mapped into the YUV color space,and then SVR is applied to build up a theoretical model between the degraded images and the original one.Performance comparisons of the proposed algorithm versus traditional filtering algorithms are given.Experimental results show that the proposed algorithm has better performance than traditional filtering algorithms and has less computation time than iterat...     

A bintree energy approach for colour image segmentation using adaptive channel selection

A new hierarchical approach called bintree energy segmentation was presented for color image segmentation. The image features are extracted by adaptive clustering on multi-channel data at each level and used as the criteria to dynamically select the best chromatic channel, where the segmentation is carried out. In this approach, an extended direct energy computation method based on the Chan-Vese model was proposed to segment the selected channel, and the segmentation outputs are then fused with other channels into new images, from which a new channel with better features is selected for the second round segmentation. This procedure is repeated until the preset condition is met. Finally, a binary segmentation tree is formed, in which each leaf represents a class of objects with a distinctive color. To facilitate the data organization, image background is employed in segmentation and channels fusion. The bintree energy segmentation exploits color information involved in all channels data and tries to optimize the global segmentation result by choosing the ＂best＂ channel for segmentation at each level. The experiments show that the method is effective in speed, accuracy and flexibility.     

Background Extraction Method Based on Block Histogram Analysis for Video Image

Introduction Withtherapiddevelopmentofvideosurveillance systemwhichisbasedoncomputervisionandimage processing,ithasbeenwidelyusedinsuchareasas shoppingcenters,airports,commercialbuildings,and congestedroads.Inmanycases,thepurposeofvideo surveillanceistode…     

Color image segmentation by edge linking and region grouping

A novel method toward color image segmentation is proposed based on edge linking and region grouping. Firstly, the edges extracted by the Canny detector are linked to form regions. Each of the end points of edges is connected by a direct line to the nearest pixel on another edge segment within a sub-window. A new distance is defined based on the feature that the edge tends to preserve its original direction. By sampling the lines to the image, the image is over-segmented to labeled regions. Secondly, the labeled regions are grouped both locally and globally. A decision tree is constructed to decide the importance of properties that affect the merging procedure. Finally, the result is refined by user’s selection of regions that compose the desired object. Experiments show that the method can effectively segment the object and is much faster than the state-of-the-art color image segmentation methods.     

基于改进塔式梯度直方图的行人检测算法

采用改进分块方式的塔式方向梯度直方图（PHOG,Pyramid Histogram of oriented gradient）作为特征提取的方法,应用支持向量机（SVM,Support Vector Machine）算法作为分类器进行训练和检测.INRIA测试集上的测试结果表明,相对于采用传统HOG和PHOG特征表示方法,所提出的方法使分类检测正确率有了进一步提高.     

基于混沌系统的量子彩色图像加密算法

为了提高图像安全性,将混沌系统引入到量子图像加密领域. 首先通过Chen混沌将图像按位异或;然后将彩色图像表示为量子的叠加态,通过Logistic混沌序列产生幺正矩阵对量子图像进行置乱;再次产生一个混沌序列对每个像素的红绿蓝三基色进行随机互换,达到对量子图像加密的目的;最后,在经典计算机上进行了模拟实验,结果表明加密后图像直方图更为平滑,像素平均分布在0～255范围内,图像相邻像素相关性低,加密图像红绿蓝像素相关系数平均值分别为0.001 6、0.001 7和0.003 8,并且密钥敏感性高,能有效抵抗穷举攻击和统计攻击,算法具有良好的有效性和可行性.