一种基于模式识别的新型双端检测器

本文提出了一种基于最近邻线分类器的新的双端检测器（DTD）。主要的思想是充分地利用特征信息以及用模式识别方法来设计DTD。本文从模式分类的角度分析了二种主要的传统DTD（Geigel和相关DTD）并给出了新的设计方法。一种称为NNL分类器的新的非参数分类器被用来检测双端通话。NNL分类器具有低运算量和优良的性能。用NNL分类器．我们熔合了几种传统的DTD并且避免了存在于大多数传统DTD中的固定阈值带来的问题。因此NNL-DTD在各种条件下是鲁棒的。实验结果也显示出了这个方法比传统方法更有效。     

基于改进的LBP人脸识别算法

针对基本LBP算子提取的特征不够完整,不能全面地表达出人脸局部特征的问题,提出了基于分块的完备局部二值模式(CLBP)人脸识别算法。首先对原始人脸图像进行分块处理,对每一分块的图像进行局部差异值和中心像素灰度值分析,用Su2CLBP(8,2)、Mu2CLBP(8,2)和CCLBP(8,2)算子分别提取每一分块的直方图统计特征。然后将所有分块的CLBP直方图序列连接起来,得到人脸图像的CLBP特征,将其作为人脸的鉴别特征用于分类识别。最后利用Chi平方统计法计算直方图的不相似度,用最近邻准则进行分类。所提出的算法分别在ORL、FERET、YALE数据库中进行实验,分别取得了高达99.5%、92%、98.67%的识别率,与分块LBP算法相比识别率分别有2.5%、8%、2.67%的提高。实验结果表明,完备LBP提取的特征比较全面且具有较强的鉴别能力,在ORL、FERET、YALE人脸库中均能获得较好的识别率。     

基于敏感特征选择与流形学习维数约简的故障诊断

针对故障诊断中特征集包含非敏感特征和维数过高的问题,提出基于特征选择(Feature selection, FS)与流形学习维数约简的故障诊断方法。提出了一种改进的核空间距离测度特征选择方法(Improved kernel distance measurement feature selection, IKMD-FS),在核空间中计算样本类间距离和类内散度,优选出使样本类间距大、类内散度小的特征,并根据特征的敏感程度对特征进行加权。通过线性局部切空间排列算法(Linear local tangent space alignment, LLTSA)对由敏感特征组成的特征子集进行特征融合,提取出对故障分类更加敏感的融合特征,并输入加权k最近邻分类器(Weighted k nearest neighbor classifier, WKNNC)进行故障识别。WKNNC具有比k最近邻分类器(k nearest neighbor classifier, KNNC)更加稳定的识别精度。最后,通过滚动轴承故障模拟实验验证了本文方法的有效性。     

改进的 AdaBoost人脸检测方法

针对传统 AdaBoost算法检测速度快准确率低的问题,本文提出了一种改进的 AdaBoost算法以提高人脸的正确检测率,该算法首先利用快速积分图提取人脸的 Haar特征,然后使用阈值设定的方法对传统的 AdaBoost算法进行改进,并将每次检测的最优弱分类器级联形成最终的强分类器,通过强弱分类器对 Haar特征判别,从而检测图像中的人脸部分。采用本方法对多种实验图像集进行人脸检测实验, FERET彩色图像库的正确检测率为96.07%,视频图像的正确检测率为 96%。实验结果表明,本文所设计的人脸检测算法能够对静态图像以及视频图像中的人脸进行有效检测,为人脸的正确识别打下了基础,该算法也为计算机视觉领域的研究提供一种有效方法。     

一种基于GDLPP的人脸识别算法

针对人脸识别中的特征提取问题,本文提出了一种结合Gabor小波特征和判别保局投影的人脸识别算法-GDLPP.该算法首先对人脸图像进行多分辨率的Gabor小波变换,提取样本的高阶统计信息;然后更改保局投影(LPP)的目标函数,增加样本类间散布约束,从而提取更具判别性的特征.本文采用最小近邻分类器估算识别率.在USPS数据库、Yale人脸库以及AR人脸库的测试结果表明,在姿态、光照、表情、训练样本数目变化的情况下,GDLPP都具有较好的识别率.     

An Efficient Detection Approach of Content Aware Image Resizing

Content aware image resizing (CAIR) is an excellent technology used widely for image retarget. It can also be used to tamper with images and bring the trust crisis of image content to the public. Once an image is processed by CAIR, the correlation of local neighborhood pixels will be destructive. Although local binary patterns (LBP) can effectively describe the local texture, it however cannot describe the magnitude information of local neighborhood pixels and is also vulnerable to noise. Therefore, to deal with the detection of CAIR, a novel forensic method based on improved local ternary patterns (ILTP) feature and gradient energy feature (GEF) is proposed in this paper. Firstly, the adaptive threshold of the original local ternary patterns (LTP) operator is improved, and the ILTP operator is used to describe the change of correlation among local neighborhood pixels caused by CAIR. Secondly, the histogram features of ILTP and the gradient energy features are extracted from the candidate image for CAIR forgery detection. Then, the ILTP features and the gradient energy features are concatenated into the combined features, and the combined features are used to train classifier. Finally support vector machine (SVM) is exploited as a classifier to be trained and tested by the above features in order to distinguish whether an image is subjected to CAIR or not. The candidate images are extracted from uncompressed color image database (UCID), then the training and testing sets are created. The experimental results with many test images show that the proposed method can detect CAIR tampering effectively, and that its performance is improved compared with other methods. It can achieve a better performance than the state-of-the-art approaches.     

Occlusion invariant face recognition using mean based weight matrix and support vector machine

In this paper, a novel occlusion invariant face recognition algorithm based on Mean based weight matrix (MBWM) technique is proposed. The proposed algorithm is composed of two phases—the occlusion detection phase and the MBWM based face recognition phase. A feature based approach is used to effectively detect partial occlusions for a given input face image. The input face image is first divided into a finite number of disjointed local patches, and features are extracted for each patch, and the occlusion present is detected. Features obtained from the corresponding occlusion-free patches of training images are used for face image recognition. The SVM classifier is used for occlusion detection for each patch. In the recognition phase, the MBWM bases of occlusion-free image patches are used for face recognition. Euclidean nearest neighbour rule is applied for the matching. GTAV face database that includes many occluded face images by sunglasses and hand are used for the experiment. The experimental results demonstrate that the proposed local patch-based occlusion detection technique works well and the MBWM based method shows superior performance to other conventional approaches.     

多彩色空间相关分析的人脸识别算法

人脸识别是当前人工智能和模式识别的研究热点,得到了广泛的关注.基于对不同色彩空间数据的分析,论文提出了多彩色空间典型相关分析的人脸识别方法.文中对2维的Contourlet变换特性进行了分析和讨论,利用Contourlet的多尺度,方向性和各向异性等特点,提出了一种基于Contourlet变换的彩色人脸识别算法.算法对原图进行Contourlet分解,对分解得到的低频和高频图像进行cca分析.典型相关分析是一种有效的分析方法,其实际应用十分广泛.低频系数反映图像的轮廓信息,高频系数反映图像的细节信息,使用cca充分利用不同频率的信息,使不同色彩空间的不同分辨率图形的相关性达到最大,得到投影系数,最后,采用决策级最近邻分类器完成人脸识别.在对彩色人脸数据库AR的识别实验中,该算法识别率达到98％以上,与传统算法相比,该算法不仅既有良好的识别结果,而且具有很快的运算速度.     

多特征双重匹配验证的驾驶员脸部融合检测

针对基于单一特征驾驶员脸部检测算法在检测精度和可靠性方面的局限性,提出了一种新颖的驾驶员脸部检测融合算法.首先采用改进的基于Haar-like特征的人脸检测算法在整幅图像上检测出可能存在的初始人脸区域,然后自适应地扩大初始人脸区域范围,并在此基础上利用基于肤色特征的方法在YCbCr空间上进行脸部的二次检测,最后根据定义的脸部区域重合度和人脸几何先验知识对驾驶员脸部区域进行双重匹配验证进而制定相应的定位规则对脸部进行融合检测.各种复杂路况下的实验结果证明了该算法的有效性.     

一种基于机器视觉的平面加工机床控制系统的设计

针对服装、包装等加工行业中须将人工测量的纸质图纸或模型样件的尺寸信息录入计算机并转换成电子加工图纸而导致的加工周期长、生产效率低的问题,提出了一种基于机器视觉的平面加工机床控制系统,以实现对纸质图纸或模型样件的快速检测。采用“ARM+DSP”方式搭建了主从式运动控制系统,设计了系统各部分功能模块。构建了“工控机+工业CCD （charge coupled device,电荷耦合器件）相机+光源控制”的视觉检测系统,结合FAWS（feature adaptive wavelet shrinkage, 自适应特征的小波收缩）算法和麻雀搜索算法提出一种改进的FAWS算法进行图像降噪,并采用Canny算法进行图像边缘检测,实现图像轮廓的准确提取。设计了图像轮廓提取、轮廓数据转换为加工数据、数据通信等处理程序,实现了基于机器视觉的快速检测以及在系统加工过程中的人机交互。最后,对系统进行了实验测试,对实际加工效果进行了评价。结果表明,采用所研制的平面加工机床控制系统不仅能显著提高生产效率,而且能减小图像轮廓的误差。其性能稳定可靠,具有一定的工程实用价值。     

基于最近邻搜索耦合近邻损耗聚类的图像伪造检测算法

目的为了解决当前图像伪造检测算法在对图像进行伪造检测时,主要依靠全局搜索的方式来完成特征点匹配,导致其检测效率较低,且在对复杂伪造图像进行检测时,易出现检测精度不高和检测错误的不足。方法提出基于最近邻搜索耦合近邻损耗聚类的图像伪造检测算法。首先引入积分图像的方法,对图像进行预处理,借助Hessian矩阵行列式来提取特征点。利用特征点构建圆形区域,通过求取圆形区域内Haar小波响应获取特征点的特征描述符。然后通过特征描述符建立KD树索引,利用最近邻搜索方法代替SURF中全局搜索的方法,对SURF进行改进,完成特征点的匹配。最后,利用特征点间的近邻关系求取近邻函数值,通过近邻函数值对特征点进行聚类,完成图像的伪造检测。结果实验结果显示,与当前图像伪造检测算法相比,所提算法具有更高的检测效率以及更高的检测正确度。结论所提算法具备较高的检测精度,在印刷防伪与信息安全等领域具有较好的应用价值。     

改进的波形复杂度算法在核爆炸监测中的应用

验证了传统的波形复杂度对于核爆地震信号分类的有效性。提出了两种时域改进算法,使识别率有了小幅度的提高。将传统的时域波形复杂度推广到时频联合域,提出了一种基于短时傅立叶变换的波形复杂度计算框架。在对所提算法框架进行简化处理后,借助于Fisher线性判别分析方法实现了地震波信号的特征提取。分类实验结果表明所提方法的分类性能优于现有的波形复杂度的分类性能。     

应用权重直方图的DW-AdaBoost快速算法

提出一种改进的AdaBoost算法,提高人脸检测的训练速度,以及检测速度和精度.先将每个Haar-Like 特征下所有样本的特征值量化,然后据此分别计算出人脸和非人脸样本,再快速计算出简单分类器的阈值和偏置.分析样本特征值的分布特性,进一步提出了双阈值快速算法.在MIT-CBCL训练库上对算法进行了验证,结果显示基于权重直方图的双阈值AdaBoost算法-DW-AdaBoost的训练速度提高150多倍,收敛速度更快.在MIT CMU人脸测试库上进行了测试,结果表明该方法在检测精度和速度等方面都优于相应的单阈值方法.     

S变换在水下目标分类识别中的应用

目标特征提取是目标识别的重要部分。介绍了一种较新的时频分析方法——S变换,对莱蒙湖底四类沉积物的反射回波进行S变换,并提出了提取变换后以频谱图的时间能量谱和奇异值为特征的特征提取方法,分析了四类回波的时间能量谱和奇异值特征的差异,并进一步用距离可分性测度检验了所提取的特征性能。最后利用最近邻分类器分类,仿真结果显示,该特征提取方法是一种有效的、稳定的特征提取方法,将在水下目标识别领域有更多的应用。     

Improving representation-based classification for robust face recognition

The sparse representation classification (SRC) method proposed by Wright et al. is considered as the breakthrough of face recognition because of its good performance. Nevertheless it still cannot perfectly address the face recognition problem. The main reason for this is that variation of poses, facial expressions, and illuminations of the facial image can be rather severe and the number of available facial images are fewer than the dimensions of the facial image, so a certain linear combination of all the training samples is not able to fully represent the test sample. In this study, we proposed a novel framework to improve the representation-based classification (RBC). The framework first ran the sparse representation algorithm and determined the unavoidable deviation between the test sample and optimal linear combination of all the training samples in order to represent it. It then exploited the deviation and all the training samples to resolve the linear combination coefficients. Finally, the classification rule, the training samples, and the renewed linear combination coefficients were used to classify the test sample. Generally, the proposed framework can work for most RBC methods. From the viewpoint of regression analysis, the proposed framework has a solid theoretical soundness. Because it can, to an extent, identify the bias effect of the RBC method, it enables RBC to obtain more robust face recognition results. The experimental results on a variety of face databases demonstrated that the proposed framework can improve the collaborative representation classification, SRC, and improve the nearest neighbor classifier.     

3D model-based pose invariant face recognition from multiple views

A 3D model-based pose invariant face recognition method that can recognise a human face from its multiple views is proposed. First, pose estimation and 3D face model adaptation are achieved by means of a three-layer linear iterative process. Frontal view face images are synthesised using the estimated 3D models and poses. Then the discriminant `waveletfaces' are extracted from these synthesised frontal view images. Finally, corresponding nearest feature space classifier is implemented. Experimental results show that the proposed method can recognise faces under variable poses with good accuracy     

A novel image analysis method for rotational motion of circular feature based on perspective projection

Abstract

Circular feature has been widely applied in recognising moving object and estimating its position and orientation. Based on the principle of perspective projection, the rotational motion of circular feature was analysed. According to whether the rotational motion occurred in the plane of circular feature, the rotational motion could be classified into two types: in-plane rotation and non-in-plane rotation. For the convenience of computation, we set the camera’s initial position and orientation. The optical axis of camera was set perpendicular to the plane of circular feature. We analysed the perspective distortion of the circular feature. Additionally, based on the geometrical reasoning, we also proposed the method of computing the rotation angles. Finally, some experiments were carried to verify correctness and feasibility of the computing method. The results showed that the measurement of rotation angle could achieve better performance though the error existed. The maximum value of errors was only 0·4°. The average errors and the standard deviations of errors were also small.     

数据特征选择与分类在机械故障诊断中的应用

针对机械故障数据的高维性和不平衡性,提出基于格拉斯曼流形的多聚类特征选择和迭代近邻过采样的故障分类方法。对采集到的振动信号,提取时域和频域相关特征,利用多聚类特征选择将高维数据以局部流形结构映射到低维特征集合。无标签样本借助迭代近邻过采样以恢复最大平衡性为目标进行样本分类,并对剩余无标签样本进行模糊分类。选取滚动轴承正常、外圈、内圈以及滚动体的故障数据,并与支持向量机、基于图的半监督学习算法进行对比。结果表明,提出的方法能有效识别出少数类故障,并在整体上有显著的分类效果。     

仿射变换下的分类识别

特征提取和分类识别是统计模式识别中两大关键步骤。显然,不同的特征提取方法与不同的分类器相结合,识别性能往往是不同的。从微分几何的角度出发,可将特征系数的获得看成线性几何变换,即仿射变换,据此在黎曼空间提出一种基于黎曼度量的分类识别方法。通过对经典最近邻分类器的线性加权,达到更有效地分类识别。不但在理论上将特征系数提取与分类识别合理的结合起来,而且由人脸识别实验表明该方法的有效性,该方法比传统方法的识别率有约 3%的提高。