一种自由手写体数字识别方法研究

提出了一种识别自由手写体数字的新方法。该方法综合利用手写字符的几何拓扑特征进行分类，然后再用自适应浮动模板描述其细特征，并进一步分类，直至识别为止。该方法对不同人书写的３０００个手写数字识别精度达９８％，识别速度为２０字／秒。     

分频匹配在语音识别与控制中的应用

语音识别的一大难题就是参数提取和最佳结果的匹配搜索。本文利用离散小波变换（DWT）特有的奇异特征提取和时变滤波功能，结合动态时间规整算法（DTW）提出了分频匹配策略，实现了在频率域上“由粗及细”地匹配，使语音词汇的识别做到更准确、更快速。实验证明了该方法的正确性和可行性。     

基于多通道Gabor滤波和特征融合的虹膜识别方法

多通道Gabor滤波器提取的虹膜特征具有冗余信息并存在部分非有效特征，针对此问题提出了改进方法。对同尺度不同方向的Gabor特征，利用幅值信息进行融合，对融合后特征进行相位编码，并运用海明距离匹配。这样，既保证了高识别性能，又将虹膜特征码压缩为传统方法的1／2，可提高匹配速度，并节约存储空间。还提出一种虹膜图像质量评价方法，可有效鉴别不适于识别的低质量虹膜图像。在CASIA和UBIRIS虹膜库的实验结果表明该方法是有效的。     

一种改进的模板匹配法在车号字符识别上的应用

本文提出一种改进的模板匹配法用来识别机车车号字符，先提取字符骨架特征进行匹配，再利用连通域特征、穿线特征和网格特征将其进行分类。实验结果表明这种方法简单、有效、实用，便于在实际中的应用，为铁路火车车号采集提高了效率，降低了成本。     

基于SVM的多生物特征融合识别算法

针对单生物特征识别的局限性,提出融合手背静脉和虹膜两种生物特征实现身份识别.基于尺度不变特征变换(SIFT)提取手背静脉的局部SIFT特征并对特征点进行匹配,利用特征匹配率作为手背静脉图像的相似度测度.通过Haar小波变换实现虹膜特征编码,利用加权汉明距对虹膜进行相似度测试.最后基于支持向量机(SVM)实现两种生物特征在匹配层的融合识别.利用CASIA虹膜数据库和TJU手背静脉数据库对算法性能进行测试,其等错率为0.02%,实验结果表明,该融合算法具有很高的识别性能,为生物特征识别研究提供了新思路.     

投影特征匹配的快速钞币面值识别算法

提出了一种基于特征区域投影特征匹配的钞币面值识别算法。针对不同面额的人民币,所选取的特征区域位置、大小和数目可不同。建立投影特征模板库,并采用分类比较和滑动匹配的方法提高算法速度和适应能力。实验结果表明:利用该算法对人民币的识别速度达到2000张/min,可满足嵌入式系统对算法实时性的要求。     

基于手背静脉虹膜和指纹融合身份识别算法

针对单模态生物特征识别的局限性,提出融合手背静脉、虹膜和指纹三种生物特征实现身份识别.首先分别对手背静脉图像、虹膜图像和指纹图像进行独立的图像预处理,特征提取和特征匹配,输出各自的匹配分数.分析匹配分数归一化对识别性能的影响,采用Tarh归一化方法对三种生物特征的匹配分数进行归一化处理,最后利用加权求和法则实现匹配分数的融合,利用最小距离分类器实现身份识别.实验结果表明,融合识别算法的等错率为0.009%,当错误接受率接近0时,对应的错误拒绝率仅为0.2%.     

双阶自适应小波聚类的航空发动机故障分类与识别

为了快速准确地实现航空发动机转子故障的分类与识别,提出了双阶自适应小波聚类方法。双阶自适应小波聚类过程是:首先采用粗网格量化数据空间,找出存在聚类的空间区域,实现数据的预分选聚类;然后统计子聚类的信息,计算其二次聚类的量化值;最后对子聚类的数据空间进行自适应细划分,实现子聚类数据空间的小波聚类。应用双阶自适应小波聚类方法对航空发动机转子的正常、不对中、碰摩、松动故障进行分类与识别,结果显示4种类型被正确分类。因此表明,对于密度分布不均匀的多类型混合数据,双阶自适应小波聚类方法能够根据数据分布特点自适应的量化网格,实现故障的正确分类与识别,诊断精度显著高于传统的小波聚类方法。     

利用小波边缘增强的可靠性匹配方法

为提高景像匹配的可靠性,提出了一种基于小波边缘增强的可靠性匹配方法。采用四阶中心B样条小波对实时图和基准图进行小波分解和边缘增强,以提取可靠的边缘特征。在粗尺度上基于小波边缘增强图进行相关匹配,选择相似度曲面上前5个峰值点作为候选匹配点,保证正确匹配点可以包含在候选匹配点中。对实时图进行旋转校正,利用候选匹配点处的局部灰度特征确定边缘提取的双阈值,应用形态学连接算子来获得二值边缘图,再进行相关匹配,筛选出正确匹配点。在细尺度上获得精确的定位点。应用该方法进行匹配实验,其匹配概率比基于小波二值边缘提取的匹配算法提高了6.95%。     

水下目标空间源数特征提取与识别方法

针对水下目标识别正横阵位问题,并进一步扩大目标识别的阵位范围。给出了目标识别的阵列形式。用水平线列阵进行目标亮点水平方位的源数估计。提取并找出了能反映水下潜艇目标空间特征的特征量。提出了基于信噪比监督的空间源数识别方法,给出了相关矩阵的空间平滑的特征值。还提出了基于信噪比和盖尔圆半径监督的目标特征的自动分类方法。对本文提出的方法进行了大量计算机仿真,仿真结果表明。该方法可在正横阵位有效识别目标。     

基于遗传算法的神经网络被动声呐目标分类研究

被动声呐目标识别系统中目标分类器的设计和训练是一项重要内容，本文设计了目标分类器的神经网络结构，提出了一种用改进的遗传算法训练神经网络分类器的新方法，最后，对海上实录的Ａ，Ｂ，Ｃ三类目标噪声进行了分类识别，实验结果表明基于遗传算法的神经网络分类器比传统的基于ＢＰ算法的神经网络分类源泛化性能有明显提高。     

基于人体手指指节折痕的身份识别方法

基于人体生物特征的认证是鉴别个人身份的有效方法.鉴于人体的手指折痕具有稳定性且对于不同的人具有不相同的特点,本文提出了一种基于手指折痕的身份识别的新方法.该算法系统由三部分组成:图像预处理、特征提取和特征匹配.在预处理阶段,提出了基于中心坐标轴的图像定位和归一化方法,并在手指内侧分割出了用于识别的长方形窗口形状的手指子图(ROI),ROI包含了手指的第一和第二折痕线;在第二阶段,提出了基于Radon变换和奇异值分解的特征提取方法;最后利用基于欧氏距离的最近邻分类器在一个取自61人共488幅手指图像的数据库上进行了匹配试验,结果验证了该方法是可行和有效的(等错误率为2.51%).     

A Two-Stage Vehicle Type Recognition Method Combining the Most Effective Gabor Features

Vehicle type recognition (VTR) is an important research topic due to its significance in intelligent transportation systems. However, recognizing vehicle type on the real-world images is challenging due to the illumination change, partial occlusion under real traffic environment. These difficulties limit the performance of current stateof-art methods, which are typically based on single-stage classification without considering feature availability. To address such difficulties, this paper proposes a twostage vehicle type recognition method combining the most effective Gabor features. The first stage leverages edge features to classify vehicles by size into big or small via a similarity k-nearest neighbor classifier (SKNNC). Further the more specific vehicle type such as bus, truck, sedan or van is recognized by the second stage classification, which leverages the most effective Gabor features extracted by a set of Gabor wavelet kernels on the partitioned key patches via a kernel sparse representation-based classifier (KSRC). A verification and correction step based on minimum residual analysis is proposed to enhance the reliability of the VTR. To improve VTR efficiency, the most effective Gabor features are selected through gray relational analysis that leverages the correlation between Gabor feature image and the original image. Experimental results demonstrate that the proposed method not only improves the accuracy of VTR but also enhances the recognition robustness to illumination change and partial occlusion.     

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.     

Multi classifier-based score level fusion of multi-modal biometric recognition and its application to remote biometrics authentication

Biometric recognition has become a common and reliable way to authenticate the identity of a person. Multimodal biometrics has become an interest of areas for researches in the recent past as it provides more reliability and accuracy. In multimodal biometric recognition, score level fusion has been a very promising approach to improve the overall system's accuracy. In this paper, score level fusion is carried out using three categories of classifiers like, rule classifier (fuzzy classifier), lazy classifier (Naïve Bayes) and learning classifiers (ABC-NN). These three classifiers have their own advantages and disadvantages so the hybridization of classifiers leads to provide overall improvements. The proposed technique consists of three modules, namely processing module, classifier module and combination module. Finally, the proposed fusion method is applied to remote biometric authentication. The implementation is carried out using MATLAB and the evaluation metrics employed are False Acceptance Rate (FAR), False Rejection Rate (FRR) and accuracy. The proposed technique is also compared with other techniques and by employing various combinations of modalities. From the results, we can observe that the proposed technique has achieved better accuracy value and Receiver Operating Characteristic (ROC) curves when compared to other techniques. The proposed technique reached maximum accuracy of having 95% and shows the effectiveness of the proposed technique.     

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。     

基于自适应粒子群算法和支持向量机的控制图模式识别

针对目前多品种、复杂化的生产趋势,提出了一种基于自适应变异的粒子群算法（AMPSO）和支持向量机（SVM）的控制图失效模式识别的方法。利用SVM小样本学习能力,设计一对一的SVM多分类器进行控制图模式识别,并利用AMPSO算法优化SVM核函数的参数。通过对10种控制图模式(6种基本模式和4种混合模式)的20维特征仿真数据对该方法进行检验,并通过与BP、SVM、PSO SVM识别方法的对比分析。仿真试验表明该方法有效提高了控制图模式的识别精度,达到9814%,而BP仅有75%,为控制图在线实时识别提供了一种可行的途径。      

基于多分类器的通信信号调制方式识别

为在恶劣接收条件下识别通信信号的调制样式,并实现识别范围的快速调整,运用决策层信息融合技术提出了一种基于多分类器的调制样式识别方法。它采用了常规特征以及三种可以快速建立的子分类器。其中“超盒”分类器用于界定各调制样式不同特征的取值范围。最近邻分类器用于分类,马氏距离分类器用于提高识别结果的可靠程度,该方法可以自动地在短时间内根据设定的识别范围建立对应的识别器,对另类调制样式或噪声有高的拒识能力。     

Automatic defect inspection system of colour filters using Taguchi-based neural network

A coloured filter is a critical part of an LCD panel, especially to present a high quality colour display. At present, the defect detection of colour filters is conducted by manual inspection in the final product stage. However, poor detection efficiency and subjective judgment of manual inspection undermine accuracy. Therefore, this study applied image processing technology and the neural network to detect surface defects of colour filters in order to prevent losses arising from incorrect detection, lower production costs, and effectively improve yield. A back-propagation neural network (BPNN) classifier was selected to train the features. The results showed that the proposed method can be successfully applied in defect detection of colour filters to reduce artificial detection errors. In addition, the Taguchi method was used with BPNN to save time searching optimal learning parameters by the trial and error method, which achieves faster convergence, smaller convergent errors and better recognition rate. The results proved that the root-mean-square error (RMSE) of the Taguchi-based BPNN at final convergence is 0.000254, and recognition rate reaches 94%. Therefore, the proposed method has good effects in detecting the micro defects of a colour filter panel.     

模糊融合分类器在水雷识别中的应用

当目标的类别多时会使分类器的精度和稳健性大受影响,用神经网络分类器去完成复杂的目标分类任务是难以保证其可靠性的。引入信息融合条件下一种新型的分类器,即模糊融合分类器,该分类器可以自动“过滤”无效和冗余特征的负面影响,实现有效的特征层融合识别。采用4种特征提取方法,利用三个模糊融合神经网络作为分类器进行特征层的融合,再将分类器的输出作为决策层的融合,提高系统的分类性能。通过处理水雷实体回波数据得出的识别率表明,所选取的特征提取和分类器算法是有效的。