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小波变换在光谱特征提取方面的应用 总被引:4,自引:1,他引:3
人们在处理高光谱图像时一般要对一些典型地物进行光谱分析、特征波段的提取,以便提取出最大量的有效信息,剔除无用或冗余的信息,然后再进行分类识别.采用小波变换的分析方法,选用合适的小波进行分解,根据分解后的高频分量中包含的重要信息,利用局部相邻的正负极值点找出对应于原始光谱曲线上每个吸收带的左右边界;利用局部过零点,即可比较精确的提取出各个吸收带的中心波长.该方法比传统的光谱特征提取方法更简洁、有效,实验证明为一种比较理想的光谱特征提取方法. 相似文献
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由于复杂环境下类烟火物体的干扰,常导致火灾检测误判。为了提高图像中火灾信号的检测精度,减少火灾误报,利用传统光谱分析在火灾图像检测技术中的优势,提出了一种基于小波变换的YOLOv5火灾检测改进算法。该算法利用二维Haar小波变换提取图像的光谱特征,将其输入到YOLOv5s的主干网络CSPDarknet中,与卷积层进行通道上的特征融合,增强烟火的纹理细节特征;通过嵌入CA注意力机制的CAC3模块,对融合小波特征后的网络层的位置信息进行增强,提高网络的信息提取和定位能力;为明确衡量边界框宽高的真实差,平衡烟火难易样本,采用α-EIOU损失函数替换原本的CIOU,提高框定位准确性。在公开的火灾数据基础上结合自制火灾数据构建火灾数据集,并进行模型训练和推理。实验结果表明,改进后算法的mAP比原YOLOv5s提升了2.3%,实现了对火灾场景烟火目标较好的检测效果。 相似文献
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邢素霞 《微电子学与计算机》2011,28(1)
首先根据小波变换原理,采用Db9小波基函数,对多组多光谱图像分别进行1~5层小波分解,然后根据小波逆变换原理对系数融合后的图像进行逆变换,得到不同小波分解层的融合图像.最后,利用图像质量评价方法信息熵、标准差、互信息、以及图像融合质量综合评价方法等,对不同分解层下的融合图像进行了评价.实验结果表明:小波变换在一层小波分解时,图像融合效果最佳. 相似文献
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Gaber小波网络能很好地提取图像特征和进行图像表达,本文提出将瞳孔位置信息引入到Gaber小波网络的人脸特征提取中以提高提取效率.该瞳孔位置信息用于两个方面,一是在网络优化时利用瞳孔位置构造T形的小波初始位置分布,使得在小波数目一定的情况下识别信息的提取更高效;二是在小波网络的参数再确定时由瞳孔位置提供定位信息从而大大简化求参步骤.本文采用Gabor小波网络提取出人脸特征后再用核联想记忆法进行分类.实验结果表明,瞳孔位置的利用提高了人脸特征提取的效率;此外,与欧氏距离、归一化互相关和最近特征线(NFL)这些方法相比,核联想记忆法具有更好的识别率. 相似文献
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对于血糖近红外无创检测.光谱信号中的各种噪声以及水分等物质的强吸收产生的背景信号,影响了光谱定量校正模型的预测精度.利用小波变换,可将光谱信号分解为多尺度的近似成分与细节成分,根据无用信息变量消除判据可判定代表背景信息的高尺度近似成分及代表噪声的低尺度细节系数,去除相应的成分即可同时去除光谱信号中的背景与噪声.本文将这种小波变换与无用信息变量消除判据相结合的预处理方法应用干人体血糖无创检测研究中,并分析了该方法对不确定因素较多的复杂光谱模型的适用性问题.实验结果表明应用小波变换结合无用信息变量消除判据的方法可以有效地同时去除血糖无创检测近红外光谱信号中的背景信息和噪声,提高光谱定量校正模型的预测精度,对于人体血糖无创检测具有重要应用价值. 相似文献
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Xiao-Ping Zhang Desai M.D. Ying-Ning Peng 《Signal Processing, IEEE Transactions on》1999,47(4):1039-1048
Previous wavelet research has primarily focused on real-valued wavelet bases. However, complex wavelet bases offer a number of potential advantageous properties. For example, it has been suggested that the complex Daubechies wavelet can be made symmetric. However, these papers always imply that if the complex basis has a symmetry property, then it must exhibit linear phase as well. In this paper, we prove that a linear-phase complex orthogonal wavelet does not exist. We study the implications of symmetry and linear phase for both complex and real-valued orthogonal wavelet bases. As a byproduct, we propose a method to obtain a complex orthogonal wavelet basis having the symmetry property and approximately linear phase. The numerical analysis of the phase response of various complex and real Daubechies wavelets is given. Both real and complex-symmetric orthogonal wavelet can only have symmetric amplitude spectra. It is often desired to have asymmetric amplitude spectra for processing general complex signals. Therefore, we propose a method to design general complex orthogonal perfect reconstruct filter banks (PRFBs) by a parameterization scheme. Design examples are given. It is shown that the amplitude spectra of the general complex conjugate quadrature filters (CQFs) can be asymmetric with respect the zero frequency. This method can be used to choose optimal complex orthogonal wavelet basis for processing complex signals such as in radar and sonar 相似文献
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一种新的小波消噪阈值的估计方法 总被引:2,自引:0,他引:2
小波多尺度分解是一种有效的信号去噪方法,对于非平稳信号的消噪,主要是选取合适的小波及每层小波系数的阈值。基于传统的阈值去噪方法,在分析研究了它们的优缺点之后,本着改进滤波效果,提高去噪质量的目的,提出了一种改进方案。该改进方案克服了传统阈值去噪方法的缺陷,并适用于进一步的自适应滤波的需求,仿真试验证实了该改进方案的有效性和优越性。 相似文献
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一种新的小波图像去噪方法 总被引:14,自引:3,他引:11
小波图像去噪已经成为目前图像去噪的主要方法之一,目前的研究主要集中于如何选取阈值使去噪达到较好的效果。边缘信息是图像最为有用的高频信息,在图像去噪的同时,应尽量保留图像的边缘信息,基于这一思想,提出一种新的小波图像去噪方法。用数学形态学算子对图像小波变换后的小波系数进行处理,以去除具有较小支持域的噪声,保留具有连续支持域的边缘。实验结果表明,与普通的小波阈值去噪方法相比,该方法不但可以保留图像的边缘信息,而且能提高去噪后图像的峰值信噪比2~5dB,提高信噪比6~10dB。 相似文献
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利用浸入式光纤采集鲜榨苹果汁分别在5mm、10mm、15mm和20mm光程下的透/反射近红外光谱,实现对苹果汁中糖度(可溶性固形物,SSC)和酸度(pH值)的定量预测.结果表明,SSC和pH具有不同的最佳光程长,分别为5mm和20mm.为了兼顾各待测量对象的浓度范围和各组分的最佳光程长,从而提高模型的性能,采用多光程光谱混合建模,研究了多光程光谱信息的提取方法.采用原始光谱直接展开所建的模型虽然能有效利用多光程光谱的信息,但增加了模型复杂度,致使建模时间增长.因此,提出了两种基于小波变换的信息提取方法,它们在高效提取多光程信息的同时,能显著缩短建模时间并简化模型.其中基于展开光谱的小波近似系数建立的模型性能最优,SSC和pH值模型的SECV值分别达到0.4761oBrix和0.0779. 相似文献
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In this paper, we develop an efficient fuzzy wavelet packet (WP) based feature extraction method for the classification of high-dimensional biomedical data such as magnetic resonance spectra. The key design phases involve: 1) a WP transformation mapping the original signals to many WP feature spaces and finding optimal WP decomposition for signal classification; 2) feature extraction based on the optimal WP decomposition; and 3) signal classification realized by a linear classifier. In contrast to the standard method of feature extraction used in WPs, guided by the criteria of signal compression or signal energy, our method is used to extract discriminatory features from the WP coefficients of the optimal decomposition. The extraction algorithm constructs fuzzy sets of features (via fuzzy clustering) to assess their discriminatory effectiveness. This paper includes a number of numerical experiments using magnetic resonance spectra. Classification results are compared with those obtained from common feature extraction methods in the WP domain. 相似文献
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This letter investigates the wavelet transform, as well as the principle and the method of the noise reduction based on wavelet
transform, it chooses the threshold noise reduction, and discusses in detail the principles, features and design steps of
the threshold method. Rigrsure, heursure, sqtwolog and minimization four kinds of threshold selection method are compared
qualitatively, and quantitatively. The wavelet analysis toolbox of MATLAB helps to realize the computer simulation of the
signal noise reduction. The graphics and calculated standard deviation of the various threshold noise reductions show that,
when dealing with the actual pressure signal of the oil pipeline leakage, sqtwolog threshold selection method can effectively
remove the noise. Aiming to the pressure signal of the oil pipeline leakage, the best choice is the wavelet threshold noise
reduction with sqtwolog threshold. The leakage point is close to the actual position, with the relative error of less than
1%. 相似文献
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S-transform-based intelligent system for classification of power quality disturbance signals 总被引:3,自引:0,他引:3
In this paper, a new approach is presented for the detection and classification of nonstationary signals in power networks by combining the S-transform and neural networks. The S-transform provides frequency-dependent resolution that simultaneously localizes the real and imaginary spectra. The S-transform is similar to the wavelet transform but with a phase correction. This property is used to obtain useful features of the nonstationary signals that make the pattern recognition much simpler in comparison to the wavelet multiresolution analysis. Two neural network configurations are trained with features from the S-transform for recognizing the waveform class. The classification accuracy for a variety of power network disturbance signals for both types of neural networks is shown and is found to be a significant improvement over multiresolution wavelet analysis with multiple neural networks. 相似文献
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通过对小波变换基线校正中最佳分解尺度方法的研究,提出了一种新的基于小波变换的最佳分解尺度确定方法,不但有效地提高了基线校正效果,而且具有简单、快速的优点.将该方法应用于血糖光谱数据预处理中进行基线校正,取得了较好的效果.通过人体口服葡萄糖耐量试验(OGTT)得到人体无创检测近红外光谱和对应血糖浓度值,然后采用该方法对上述光谱进行基线校正并建立多元回归模型,采用交互验证的方式对模型及基线校正的效果进行了评价.实验结果表明,血糖浓度预测值和参考值间的相关系数为0.75,预测均方根误差(RMSEP)为1.36 mmol/L,与原始光谱预测结果和其他小波分解尺度下的预测结果相比,RMSEP降低了将近39%,相关系数提高了0.64,预测精度得到较大幅度提高. 相似文献
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Using wavelet transform and fuzzy neural network for VPC detection from the Holter ECG 总被引:2,自引:0,他引:2
A novel method for detecting ventricular premature contraction (VPC) from the Holter system is proposed using wavelet transform (WT) and fuzzy neural network (FNN). The basic ideal and major advantage of this method is to reuse information that is used during QRS detection, a necessary step for most ECG classification algorithm, for VPC detection. To reduce the influence of different artifacts, the filter bank property of quadratic spline WT is explored. The QRS duration in scale three and the area under the QRS complex in scale four are selected as the characteristic features. It is found that the R wave amplitude has a marked influence on the computation of proposed characteristic features. Thus, it is necessary to normalize these features. This normalization process can reduce the effect of alternating R wave amplitude and achieve reliable VPC detection. After normalization and excluding the left bundle branch block beats, the accuracies for VPC classification using FNN is 99.79%. Features that are extracted using quadratic spline wavelet were used successfully by previous investigators for QRS detection. In this study, using the same wavelet, it is demonstrated that the proposed feature extraction method from different WT scales can effectively eliminate the influence of high and low-frequency noise and achieve reliable VPC classification. The two primary advantages of using same wavelet for QRS detection and VPC classification are less computation and less complexity during actual implementation. 相似文献