共查询到19条相似文献,搜索用时 250 毫秒
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针对基于小波能量谱和能量谱熵的故障诊断方法要求小波分解系数基本符合高斯分布这一不足,提出一种基于多尺度小波域隐马尔可夫模型(WHMM)参数特征的故障诊断方法.该方法分析了信号多尺度小波分解系数的统计特征,利用隐马尔可夫模型描述小波变换域系数在尺度间,尺度内的统计相关性.采用最大似然估计方法确定的模型参数作为信号特征实现故障诊断.试验结果证实了设计思想的正确性和算法的高效检测性能.最后从小波基、窗口宽度和分类器三个层面对建议方法诊断性能的影响进行分析,结果表明本文方法具有很强的稳定性和鲁棒性. 相似文献
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摘要:含金属芯压电纤维(Metal-core Piezoelectric Ceramic Fiber,MPF)是一种新型压电功能器件。介绍了MPF的结构及其对圆形压电片激励Lamb波的传感响应模型。利用Gabor小波变换计算损伤反射信号到达时间延迟的原理,把MPF传感单一模式Lamb波在一维结构中进行了损伤定位研究。研究结果表明:MPF可以进行Lamb波的单一模式传感,采用Gabor小波变换计算损伤反射信号到达时间延迟效果较好,损伤定位精度较高。 相似文献
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旋转不变纹理特征用于两级图像检索 总被引:4,自引:2,他引:2
针对图像中常见的旋转问题提出一种旋转不变纹理特征进行两级图像检索的方法。粗检中,通过坐标变换把图像的旋转转换为行移,并提取近似行移不变的小波特征,结合粗比较算法对整个图像库进行粗检。然后对通过粗检的图像进行 Gabor 变换,提取旋转不变精检索特征,并使用Canberra 距离进行相似性度量。通过对旋转图像库的测试表明,该方法不仅加快了运算速度,且当参数选择适当时,在相同特征条件下,检索率比直接使用精检索方法检索时还提高了 1.625%。 相似文献
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小波包变换和隐马尔可夫模型在轴承性能退化评估中的应用 总被引:4,自引:4,他引:0
轴承是旋转机械中的关键部件,有效地对其进行性能退化评估对指导设备维护、防止设备意外失效有非常重要的意义。本文提出了一种基于小波包变换和隐马尔可夫模型(HMM)的轴承性能退化评估方法。该方法使用小波包变换对轴承振动信号进行分析,并提取节点能量及其总能量作为特征,仅使用正常状态下的数据训练HMM,建立性能退化评估模型,然后使用该模型对轴承的退化程度进行定量评估。最后,通过对轴承加速疲劳寿命试验的研究,验证了所提出的方法的可行性和有效性。 相似文献
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分形与小波相结合的鲁棒性数字水印算法 总被引:4,自引:4,他引:0
为了提高图像水印算法的鲁棒性,提出了一种分形与小波相结合的鲁棒性数字水印算法。该算法按密钥把水印信息转化为自相似分形图;对宿主图像进行离散Haar小波变换,然后将分形后的二值水印图像嵌入到Haar小波低频子带。仿真实验证明,该算法对噪声J、PEG压缩、剪切、平滑滤波等图像失真处理,均具有很好的鲁棒性。 相似文献
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工业内窥镜图像对比度增强算法 总被引:3,自引:0,他引:3
针对内窥镜图像由于照明等原因而造成的亮度不均匀,局部对比度较低的问题,本文提出一种基于小波变换的同态滤波算法对内窥镜图像进行对比度增强.分析了照明反射模型的基本原理及可用于消除光照不均的同态滤波方法,采用快速小波变换代替传统傅里叶变换,在变换域中对小波系数进行非线性增强,对不同尺度上的小波系数进行不同程度的增强.实验结果表明,该方法可以有效消除由光照不均匀引起的图像亮度不均匀,增强图像对比度的同时不改变图像的原始面貌,其效果优于传统的同态滤波方法. 相似文献
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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. 相似文献
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在纹理分类中采用谱直方图表示(SHR),每个图像窗表示一个包含滤波后图像直方图的特征向量,而直方图是图像谱表示的连接桥梁.在滤波器选择算法之前,结合每个图像分块和滤波器的独立谱表示和直方图,可以获得更加低层的局部特征.最后,时所有独立滤波器采用滤波器选择算法来得到所需的少量滤波器.为了保证分类的可靠性,选择高斯径向基函数(RBF)进行谱直方图表示,采用支持向量机(SVMs)作为分类函数.对本文方法和其它两种方法:Gabor滤波和独立成分分析(ICA)进行了纹理分类和脸部识别的比较实验.实验结果表明,本文方法具有更高的分类准确性,也证明了SVMs优秀的泛化能力. 相似文献
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In biometrics, face recognition is one of the important identification methods with various applications such as, video surveillance, defence, human/computer interactions and many more. The current face recognition systems perform well using the frontal images with high resolution. In contrast, the utilisation of low-resolution (LR) images degrades the performance of face recognition systems. Hence, this paper integrates the Gabor filter?+?wavelet?+?texture (GWTM) operator and the BAT algorithm to increase the performance, while deploying the LR images. The proposed algorithm integrates the uniqueness of Gabor features, the robustness of local features and the wavelet features to handle the inter-person and intra-person variations. This paper utilises the spherical SVM classifier to enhance the recognition performance. Finally, the proposed GWTM operator is compared with other existing algorithms such as, GOM, LBP and LGP based on the parameters of accuracy, FAR and FRR. The proposed GWTM operator attains the highest accuracy of 95% and a minimum FAR of 5%. The results prove that the proposed GWTM yields a performance improvement of 5, 3, 4 and 15% over the GOM, LBP, LGP and GWTM, respectively, in the absence of the BAT algorithm. 相似文献
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Hu‐Chuan Lu Ying‐Jie Huang Yen‐Wei Chen 《International journal of imaging systems and technology》2010,20(3):253-260
PCA, ICA, and Gabor wavelet are considered as the important and powerful face representation methods. In this article, we propose a new approach for face representation, which is called a pixel‐pattern‐based texture feature (PPBTF) and apply it to the real‐time facial expression recognition. A gray scale image is transformed into a pattern map where edges and lines are used for characterizing the facial texture information. Based on the pattern map, a feature vector is comprised of the numbers of the pixels belonging to each pattern. We use the image basis functions obtained by principal component analysis as the templates for pattern matching. Adaboost and Support Vector Machine are adopted to classify facial expression. Extensive experiments on the Cohn‐Kanade Database, PIE Database, and DUT Database illustrate that the PPBTF is quite effective and insensitive to illumination. The comparison with Gabor show the PPBTF is speedy. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 253–260, 2010 相似文献
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基于2DGabor变换的人脸特征描述已经受到了很多人的关注。然而现有的Gabor特征维数较高,而且具有冗余性,因此选择最佳的Gabor特征用于人脸识别显得尤为的重要。利用最大余量原理的特征选择算法在目前的机器学习研究中已经占据了重要的地位。本文在基于余量的迭代搜索法(Simba)的基础上,引入了一种新的选择算法:基于余量的共轭梯度法(Cgmba),它只需较少次迭代就可以找到最佳解。我们在IMM人脸库上进行了实验,实验结果表明:尽管只使用了一半不到的特征,但Cgmba和Simba的识别率却分别提高了3.75和1.25个百分点,同时也证实了我们提出的Cgmba明显优于Simba。最后我们对Cgmba选择的Gabor特征的分布情况进行了分析,可以看出较大尺度的特征相对于较小尺度的特征对于分辩人脸的细微差别具有同等的重要性,而且在垂直,135°方向的特征具有更强的分辩能力。 相似文献
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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. 相似文献
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采用图像融合技术的多模式人脸识别 总被引:2,自引:0,他引:2
利用图像融合技术实现了基于可见光图像和红外热图像相结合的多模式人脸识别,研究了两种图像在像素级和特征级的融合方法.在像素级,提出了基于小波分解的图像融合方法,实现了两种图像的有效融合.在特征级,采用分别提取两种识别方法中具有较好分类效果的前50%的特征进行特征级的融合.实验表明,经像素级和特征级融合后,识别准确率都较单一图像有很大程度的提高,并且特征级的融合效果明显优于像素级的融合.因此,基于图像融合技术的多模式人脸识别,有效的增加了图像的信息量,是提高人脸识别准确率的有效途径之一. 相似文献