基于SVM的车牌字符识别算法研究

SVM可在训练样本很少的情况下获得很好的分类推广能力。首先分析了用多类SVM算法对车牌中的字符进行识别时存在不可区分的区域问题和采用模糊SVM算法解决该问题的办法,然后讨论了字符特征的提取方法,并根据我国车牌字符的特点分别设计了汉字、字母、数字、字母／数字4个基于模糊多类SVM的字符分类器。最后在MATLAB环境下,采用径向基核函数对算法进行学习训练。实验测试结果表明,该方法可以很好的提高字符识别的速率和效率。     

基于支撑向量机和小波的字符识别

为了提高车牌上的字符识别准确率，提出一种结合支撑矢量机(SVM)和小波的字符识别方法.通过对
字符图像水平和垂直两个方向的投影曲线分别进行小波分解，得到投影曲线的近似表示.在近似曲线中提
取字符的特征参数，用这些特征参数构成特征矢量作为SVM训练和分类的基本参数，再将特征矢量输入支
撑矢量机网络训练，最后通过树型分类识别模型识别字符.实验仿真表明，该字符识别方法的平均准确率
为97.15%，平均识别速度为每个字符19.15 ms.     

基于判别性区域提取的视频人体动作识别方法

针对全局特征描述过分依赖精确定位、背景减除和跟踪技术等问题,同时也为了解决视角变化、噪声和遮挡等干扰带来的影响,对基于局部特征描述的视频人体动作识别方法进行了研究,提出了一种基于判别性区域提取的视频人体动作识别方法.首先通过迭代训练和筛选过程对视频的内容进行分析和学习,自动提取视频中有代表性和区分性的判别性区域,然后使用词袋模型对提取到的判别性区域进行统计和描述,最后采用支持向量机方法确定人体运动的类型.在KTH和Youtube数据集上分别对提出的方法进行了论证,结果表明:该方法具有较高的识别准确率,同时对复杂背景等干扰不敏感.     

基于Gabor变换和支持向量机的车牌字符识别算法

为了提高车牌字符识别率，将支持向量机SVM方法用于车牌字符的识别，算法首先采用Gabor变换和整体结构特征提取的方法提取车牌字符图像的特征参数，然后采用提取的特征训练SVM分类器，再应用SVM分类器分类和判别车牌字符。实验表明这种方法具有良好的车牌识别效果，较强的鲁棒性，有较大的应用价值。     

A Scheme of sEMG Feature Extraction for Improving Myoelectric Pattern Recognition

This paper proposed a feature extraction scheme based on sparse representation considering the non-stationary property of surface electromyography (sEMG). Sparse Bayesian Learning (SBL) algorithm was introduced to extract the feature with optimal class separability to improve recognition accuracies of multi-movement patterns. The SBL algorithm exploited the compressibility (or weak sparsity) of sEMG signal in some transformed domains. The proposed feature extracted by using the SBL algorithm was named SRC. The feature SRC represented time-varying characteristics of sEMG signal very effectively. We investigated the effect of the feature SRC by comparing with other fourteen individual features and eighteen multi-feature sets in offline recognition. The results demonstrated the feature SRC revealed the important dynamic information in the sEMG signals. And the multi-feature sets formed by the feature SRC and other single features yielded more superior performance on recognition accuracy. The best average recognition accuracy of 91.67% was gained by using SVM classifier with the multi-feature set combining the feature SRC and the feature wavelength (WL). The proposed feature extraction scheme is promising for multi-movement recognition with high accuracy.     

支持向量机在字符分类识别中的应用

为了对数字字符和字母字符进行有效识别，提出了一种利用二值字符图像投影的特征参数构
造字符特征矢量的方法，对这些特征矢量进行归一化处理并作为支持向量机的训练集。采用支持向量机和
多层感知器网络对字符的特征矢量进行训练，分别构造出26个字母分类器、10个数字分类器以及36个字母
-数字综合分类器。通过对字符的分类识别测试，字符识别的准确率平均为96.5％，识别速度平均为20.5
ms/字符，结果表明了支持向量机在字符识别应用中的有效性。     

基于降采样归一化割的多尺度分层分割方法研究

针对全局特征描述过分依赖精确定位、背景减除和跟踪技术等问题,同时也为了解决视角变化、噪声和遮挡等干扰带来的影响,对基于局部特征描述的视频人体动作识别方法进行了研究,提出了一种基于判别性区域提取的视频人体动作识别方法.首先通过迭代训练和筛选过程对视频的内容进行分析和学习,自动提取视频中有代表性和区分性的判别性区域,然后使用词袋模型对提取到的判别性区域进行统计和描述,最后采用支持向量机方法确定人体运动的类型.在KTH和Youtube数据集上分别对提出的方法进行了论证,结果表明：该方法具有较高的识别准确率,同时对复杂背景等干扰不敏感.

     

基于连续小波变换的湖底回波特征提取

提出了以多尺度连续小波变换值矩阵的奇异值作为识别特征矢量的方法,并利用该方法对湖底回波实测数据进行特征提取与分类。理论分析与仿真试验结果表明,相对于时间-小波能量和尺度-小波能量特征提取法而言,该方法可得到更好的类内紧致性和类间可分性,以及更佳的分类效果。     

Mandarin Digits Speech Recognition Using Support Vector Machines

A method of applying support vector machine (SVM) in speech recognition was proposed, and a speech recognition system for mandarin digits was built up by SVMs. In the system, vectors were linearly extracted from speech feature sequence to make up time-aligned input patterns for SVM, and the decisions of several 2-class SVM classifiers were employed for constructing an N-class classifier. Four kinds of SVM kernel functions were compared in the experiments of speaker-independent speech recognition of mandarin digits. And the kernel of radial basis function has the highest accurate rate of 99.33 %, which is better than that of the baseline system based on hidden Markov models (HMM) (97.08%). And the experiments also show that SVM can outperform HMM especially when the samples for learning were very limited.     

一种边缘二进制码的表情识别方法

针对如何从图像中提取有效的表情特征来提高表情识别率的问题,提出了一种基于边缘二进制码的表情特征提取方法,用于表情识别.该方法首先对图像进行边缘检测,然后对边缘的局部结构进行二进制码描述作为表情特征,最后利用支持向量机进行表情分类.在JAFFE人脸表情数据库上,分别用该方法和传统的方法进行试验,结果表明,该方法可显著提高表情识别率.     

基于小波提取特征的SVM目标识别

基于小波变换提出了一种特征提取及特征选择的方法.通过对小波系数绝对值化,减小了特征的分布范围;对小波进行自适应的过滤提取了目标的主要特征,增加了特征的聚类程度.本文通过SVM分类器对该方法进行验证并与其他方法比较.实验证明该方法有效的提高了目标的识别率,降低了误识别率.     

SVM with discriminative dynamic time alignment

In the past several years, support vector machines (SVM) have achieved a huge success in many fields, especially in pattern recognition. But the standard SVM cannot deal with length-variable vectors, which is one severe obstacle for its applications to some important areas, such as speech recognition and part-of-speech tagging. The paper proposed a novel SVM with discriminative dynamic time alignment (DDTA-SVM) to solve this problem. When training DDTA-SVM classifier, according to the category information of the training samples, different time alignment strategies were adopted to manipulate them in the kernel functions, which contributed to great improvement for training speed and generalization capability of the classifier. Since the alignment operator was embedded in kernel functions, the training algorithms of standard SVM were still compatible in DDTA-SVM. In order to increase the reliability of the classification, a new classification algorithm was suggested. The preliminary experimental results on Chinese confusable syllables speech classification task show that DDTA-SVM obtains faster convergence speed and better classification performance than dynamic time alignment kernel SVM (DTAK-SVM). Moreover, DDTA-SVM also gives higher classification precision compared to the conventional HMM. This proves that the proposed method is effective, especially for confusable length-variable pattern classification tasks.     

一种改进卷积神经网络的教学图像检索方法

针对卷积神经网络在提取图像特征时所造成的特征信息损失以及降低高维度图像特征数据等问题,提出了一种改进卷积神经网络的图像检索优化方法。该方法首先利用融合的卷积层提取图像特征,并在融合的卷积层之间添加全连接层以减少特征信息的丢失;然后采用主成分分析法对高维的特征数据进行有效的降维处理;最后采用余弦相似度的方法进行相似度匹配,以实现相似图像的检索。采用当前经典的LeNet-L、LeNet-5等方法同文中方法在图像检索性能评价指标上进行对比实验。实验结果表明,所提出的检索方法比文中其他检索方法在查全率和平均查准率方面提高了3%27.3%。     

一种具有统计不相关性的最优边界鉴别向量集

为了解决现有维数约简算法受样本分布影响较大、不支持小样本学习等问题,在分析线性鉴别分析(LDA)中最优鉴别向量与支持向量机(SVM)中分类超平面法向量之间关系的基础上,基于统计不相关最优鉴别向量集优于正交最优鉴别向量集的事实,提出了通过对改进的SVM的二次优化问题进行递归求解来获取具有统计不相关性的最优边界鉴别向量集的算法,并使用核方法将其推广到可以解决非线性特征抽取问题.结果表明:在采用相同参数并使用k-最近邻分类器进行训练和测试的情况下,提出的算法对实际数据集Waveform,Heart,Diabetis的分类精度均高于SVM和RSVM,不会出现当抽取超过最优维数时随着抽取维数的增加分类精度反而降低的现象,体现了本算法在抽取不相关特征向量方面的有效性.     

Image Classification Based on the Fusion of Complementary Features

Image classification based on bag-of-words (BOW) has a broad application prospect in pattern recognition field but the shortcomings such as single feature and low classification accuracy are apparent. To deal with this problem, this paper proposes to combine two ingredients:(i) Three features with functions of mutual complementation are adopted to describe the images, including pyramid histogram of words (PHOW), pyramid histogram of color (PHOC) and pyramid histogram of orientated gradients (PHOG). (ii) An adaptive feature-weight adjusted image categorization algorithm based on the SVM and the decision level fusion of multiple features are employed. Experiments are carried out on the Caltech 101 database, which confirms the validity of the proposed approach. The experimental results show that the classification accuracy rate of the proposed method is improved by 7%-14% higher than that of the traditional BOW methods. With full utilization of global, local and spatial information, the algorithm is much more complete and flexible to describe the feature information of the image through the multi-feature fusion and the pyramid structure composed by image spatial multi-resolution decomposition. Significant improvements to the classification accuracy are achieved as the result.     

利用密度聚类支持向量机的气象云图云检测

为了提高气象云图云检测的判识精度和计算效率,提出一种基于密度聚类支持向量机(DC-SVM)的云检测方法。分析了MTSAT气象云图的特征提取和选择方案,建立了云和下垫面的分类样本集;在SVM学习中,通过引入样本集的纯度及充足度,选择关键样本,减少了噪声和异常样本的干扰,从而降低了计算复杂度,提高了分类精度。实验表明,该算法的分类正确率较BP神经网络及传统SVM的方法分别提高了2.54%和0.21%,训练时间及测试时间也明显减少;而且,该方法还克服了传统云检测方法需要根据先验知识确定阈值的缺点,检测结果与人工解译结果基本吻合。     

Chinese Named Entity Recognition with Character-Level BLSTM and Soft Attention Model

Unlike named entity recognition( NER) for English,the absence of word boundaries reduces the final accuracy for Chinese NER. To avoid accumulated error introduced by word segmentation,a deep model extracting character-level features is carefully built and becomes a basis for a new Chinese NER method,which is proposed in this paper. This method converts the raw text to a character vector sequence,extracts global text features with a bidirectional long short-term memory and extracts local text fea...     

Feature-Based Fusion of Dual Band Infrared Image Using Multiple Pulse Coupled Neural Network

To improve the quality of the infrared image and enhance the information of the object, a dual band infrared image fusion method based on feature extraction and a novel multiple pulse coupled neural network (multi-PCNN)is proposed. In this multi-PCNN fusion scheme, the auxiliary PCNN which captures the characteristics of feature image extracting from the infrared image is used to modulate the main PCNN, whose input could be original infrared image. Meanwhile, to make the PCNN fusion effect consistent with the human vision system, Laplacian energy is adopted to obtain the value of adaptive linking strength in PCNN. After that, the original dual band infrared images are reconstructed by using a weight fusion rule with the fire mapping images generated by the main PCNNs to obtain the fused image. Compared to wavelet transforms, Laplacian pyramids and traditional multi-PCNNs, fusion images based on our method have more information, rich details and clear edges.     

基于KPCA和SVM的火箭发动机试验台故障诊断方法

为了解决液体火箭发动机试验台的故障诊断问题,提出了一种基于核主元分析(KPCA)特征提取和支持向量多分类机(SVM)的故障诊断方法,该方法首先利用核主元分析对试验台标准故障样本进行特征提取,通过特征分析,建立适合于试验台故障状态识别的层次多分类支持向量机,并对其进行训练,然后将试验数据在主元上投影,输入到训练好的支持向量多分类器,对试验台故障状态进行识别.该方法充分利用了核主元分析强大的非线性特征提取能力和支持向量分类机良好的小样本泛化特性,解决了试验台故障诊断中的小样本、非线性模式识别问题.对试验台的试验结果表明,该方法是有效的、可行的.     

产品内部构件状态的自动检测方法

目的研究复杂产品内部构件状态的自动检测方法．方法依据Ｘ射线成像和数字图像处理技术,通过对预处理后标准样本图像的特征提取,得到复杂产品内部构件的状态特征,并采用模式识别方法由这些状态特征建立状态识别模型,依据识别模型,完成产品内部构件状态的自动分类识别．结果利用所建立的特征库和构件状态识别模型,提出了一种构件状态自动无损检测方法．结论该方法对于自动检测复杂产品内部构件状态具有普遍意义．