Multiscale texture classification using reduced quaternion wavelet transform

This article proposes a study of the reduced quaternion wavelet transform (RQWT) which has one shift-invariant magnitude and three angle phases at each scale from digital image analysis application. A new multiscale texture classifier which uses features extracted from the sub-bands of the RQWT decomposition is proposed in the transform domain. The proposed method can achieve a high texture classification rate. The experimental results can demonstrate the robustness of the proposed method and achieve a higher texture classification accuracy rate than a famous wavelet transform based classifier.     

Spike detection using the continuous wavelet transform

This paper combines wavelet transforms with basic detection theory to develop a new unsupervised method for robustly detecting and localizing spikes in noisy neural recordings. The method does not require the construction of templates, or the supervised setting of thresholds. We present extensive Monte Carlo simulations, based on actual extracellular recordings, to show that this technique surpasses other commonly used methods in a wide variety of recording conditions. We further demonstrate that falsely detected spikes corresponding to our method resemble actual spikes more than the false positives of other techniques such as amplitude thresholding. Moreover, the simplicity of the method allows for nearly real-time execution.     

Embolic Doppler ultrasound signal detection using discrete wavelet transform

Asymptomatic circulating emboli can be detected by Doppler ultrasound. Embolic Doppler ultrasound signals are short duration transient like signals. The wavelet transform is an ideal method for analysis and detection of such signals by optimizing time-frequency resolution. We propose a detection system based on the discrete wavelet transform (DWT) and study some parameters, which might be useful for describing embolic signals (ES). We used a fast DWT algorithm based on the Daubechies eighth-order wavelet filters with eight scales. In order to evaluate feasibility of the DWT of ES, two independent data sets, each comprising of short segments containing an ES (N=100), artifact (N=100) or Doppler speckle (DS) (N=100), were used. After applying the DWT to the data, several parameters were evaluated. The threshold values used for both data sets were optimized using the first data set. While the DWT coefficients resulting from artifacts dominantly appear at the higher scales (five, six, seven, and eight), the DWT coefficients at the lower scales (one, two, three, and four) are mainly dominated by ES and DS. The DWT is able to filter out most of the artifacts inherently during the transform process. For the first data set, 98 out of 100 ES were detected as ES. For the second data set, 95 out of 100 ES were detected as ES when the same threshold values were used. The algorithm was also tested with a third data set comprising 202 normal ES; 198 signals were detected as ES.     

Noise-robust pitch detection method using wavelet transform with aliasing compensation

An improved wavelet-based method is developed for extracting pitch information from noisy speech. It uses a modified spatial correlation function which is originally applied to wavelet-based signal denoising to improve the performance of pitch detection in a noisy environment. The modified spatial correlation function needed in the proposed pitch detection method makes use of an aliasing compensation algorithm to eliminate the aliasing distortion that arises from the downsampling and upsampling operations of the wavelet transform. As a consequence, this allows one to further increase the accuracy of pitch detection. It is shown in various experimental results that this new method gives a considerable performance improvement when compared with other conventional and wavelet-based methods.     

Image coding using wavelet transform

A scheme for image compression that takes into account psychovisual features both in the space and frequency domains is proposed. This method involves two steps. First, a wavelet transform used in order to obtain a set of biorthogonal subclasses of images: the original image is decomposed at different scales using a pyramidal algorithm architecture. The decomposition is along the vertical and horizontal directions and maintains constant the number of pixels required to describe the image. Second, according to Shannon's rate distortion theory, the wavelet coefficients are vector quantized using a multiresolution codebook. To encode the wavelet coefficients, a noise shaping bit allocation procedure which assumes that details at high resolution are less visible to the human eye is proposed. In order to allow the receiver to recognize a picture as quickly as possible at minimum cost, a progressive transmission scheme is presented. It is shown that the wavelet transform is particularly well adapted to progressive transmission.     

Multiscale characterization of chronobiological signals based on the discrete wavelet transform

To compensate for the deficiency of conventional frequency-domain or time-domain analysis, this paper presents a multiscale approach to characterize the chronobiological time series (CTS) based on a discrete wavelet transform (DWT). We have shown that the local modulus maxima and zero-crossings of the wavelet coefficients at different scales give a complete characterization of rhythmic activities. We further constructed a tree scheme to represent those interacting activities across scales. Using the bandpass filter property of the DWT in the frequency domain, we also characterized the band-related activities by calculating energy in respective rhythmic bands. Moreover, since there is a fast and easily implemented algorithm for the DWT, this new approach may simplify the signal processing and provide a more efficient and complete study of the temporal-frequency dynamics of the CTS. Preliminary results are presented using the proposed method on the locomotion of mice under altered lighting conditions, verifying its competency for CTS analysis.     

基于小波变换的语音基频提取新算法

该文将小波变换应用于具有连续语音特征的三字词语音的基频提取,并针对实验中出现的问题对算法进行了改进,提出了一种新的基于小波变换的语音基频检测算法。该算法主要包括:离散小波变换计算、基于投票策略的基频点选择和基频起点确定、基频检查、异常点修正、头尾漏点处理以及基于投票策略的基频点精确定位。实验表明,该算法较好地克服了基于小波变换传统算法的不足,更适合于连续语音的基频提取,缺陷是需要较多的计算时间,不太适合于实时性要求较高的系统。     

基于小波分析的倒装芯片主动红外缺陷检测

随着微电子技术的需求和发展,倒装芯片技术在高密度微型化封装领域得到了快速发展和广泛应用,而现有的一些倒装芯片检测方法存在一定的不足之处。为此,研究了主动红外的倒装芯片缺陷检测方法。实验中使用激光加热对倒装样片施加非接触热激励,通过红外热像仪获取样片温度分布。采用小波分析方法提取包括小波熵在内的信号特征,采用自组织神经网络对不同类型焊球进行聚类识别。研究表明,通过自组织神经网络可以有效地将不同缺陷焊球与参考焊球通过距离映射法映射到不同区域从而区分开,并且可以将未知焊球信号映射到相应的区域实现聚类识别。因此该方法可以有效实现倒装芯片的缺陷检测。     

Fault detection and isolation using concatenated wavelet transform variances and discriminant analysis

A method for fault detection and isolation is developed using the concatenated variances of the continuous wavelet transform (CWT) of plant outputs. These concatenated variances are projected onto the principal component space corresponding to the covariance matrix of the concatenated variances. Fisher and quadratic discriminant analyses are then performed in this space to classify the concatenated sample CWT variances of outputs in a given time window. The sample variance is a variance estimator obtained by taking the displacement average of the squared wavelet transforms of the current outputs. This method provides an alternative to the multimodel approach used for fault detection and identification, especially when system inputs are unmeasured stochastic processes, as is assumed in the case of the mechanical system example. The performance of the method is assessed using matrices having the percentage of correct condition identification in the diagonal and the percentages misclassified conditions in the off-diagonal elements. Considerable performance improvements may be obtained due to concatenation-when two or more outputs are available-and to discriminant analysis, as compared with other wavelet variance methods.     

Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach

Early and accurate detection of myocardial infarction is imperative for reducing the mortality rate due to heart attack. Present work proposes a novel technique aiming toward accurate and timely detection of inferior myocardial infarction (IMI). Stationary wavelet transform has been used to decompose the segmented multilead electrocardiogram (ECG) signal into different sub-bands. Sample entropy, normalized sub-band energy, log energy entropy, and median slope calculated over selected bands of multilead ECG are used as features. Support vector machine (SVM) and K-nearest neighbor (KNN) have been used to classify between subjects admitted for health control (HC) and patients suffering from IMI, using attributes selected on the basis of gain ratio. The full length ECG of lead II, III, and aVF of all the subjects having IMI or admitted for HC from Physikalisch-Technische Bundesanstalt Database (PTB-DB) has been used in the present work. The proposed technique has been scrutinized under both “class-oriented,” and more practical, “subject-oriented” approach. Under the class-oriented approach, data have been divided into training and test data irrespective of the patients, whereas in subject-oriented approach, data from one patient have been used for test and training has been done on the rest of the subjects. Under the class-oriented approach, area under the receiver operating characteristic curve (Roc), sensitivity (Se%), specificity (Sp%), positive predictivity (+P%), and accuracy (Ac%) is Roc \(=\) 0.9945, Se% \(=\) 98.67, Sp% \(=\) 98.72, +P% \(=\) 98.79, Ac% \(=\) 98.69 using KNN and Roc \(=\) 0.9994, Se% \(=\) 99.35, Sp% \(=\) 98.29, +P% \(=\) 98.41, Ac% \(=\) 98.84 using SVM. For the subject-oriented approach, an average Ac% \(=\) 81.71, Se% \(=\) 79.01, Sp% \(=\) 79.26, and +P% \(=\) 80.25 has been achieved. This shows the potential of the proposed technique to work for an unknown subject, on which it has not been trained.     

基于小波变换的图像边缘检测

介绍了利用小波变换进行图像边缘检测的原理与方法。基于小波变换的模极大值原理,利用不同尺度小波变换后的不同方向获取图像的高频信息,并通过小波系数的模极值点与过零点,检测出图像在四个方向上的模极大值,得到该位置模的局部最大值。仿真测试表明,利用小波变换进行图像边缘检测可以较好的检测图像边缘的细节特征,取得了很好的效果。     

基于小波变换的图像边缘检测

介绍了利用小波变换进行图像边缘检测的原理与方法。基于小波变换的模极大值原理,利用不同尺度小波变换后的不同方向获取图像的高频信息,并通过小波系数的模极值点与过零点,检测出图像在四个方向上的模极大值,得到该位置模的局部最大值。仿真测试表明,利用小波变换进行图像边缘检测可以较好的检测图像边缘的细节特征,取得了很好的效果。     

基于小波变换的QRS波群检测

提出了一种基于小波多分辨分析的算法,对心电信号进行特征提取和识别。通过小波变换对常规心电图信号进行分解去噪和特征提取,并利用动态自适应阈值和删除多检点,补偿漏检点对QRS波检测进行优化。实验结果表明该方法在QRS波形不失真的情况下,提高了一部分MIT-BIH数据库信号中QRS波识别的准确率,并且对于较低准确率的心电信号的原因进行了分析。     

Multiscale contrast enhancement for radiographies: Laplacian Pyramid versus fast wavelet transform

Contrast enhancement of radiographies based on a multiscale decomposition of the images recently has proven to be a far more versatile and efficient method than regular unsharp-masking techniques, while containing these as a subset. In this paper, we compare the performance of two multiscale-methods, namely the Laplacian Pyramid and the fast wavelet transform (FWT). We find that enhancement based on the FWT suffers from one serious drawback-the introduction of visible artifacts when large structures are enhanced strongly. By contrast, the Laplacian Pyramid allows a smooth enhancement of large structures, such that visible artifacts can be avoided. Only for the enhancement of very small details, for denoising applications or compression of images, the FWT may have some advantages over the Laplacian Pyramid.     

Texture segmentation using modulated wavelet transform

The wavelet (packet) transform has been widely used for texture analysis; however, the extracted features of similar textures with symmetric orientations are indistinguishable. Motivated by the AM-FM representation, the so called modulated wavelet (packet) transform that can be implemented efficiently by the conventional pyramid (tree) structured algorithms is developed. The performance of this new transform is demonstrated on the segmentation of Brodatz (1966) textures and an aerial image of San Francisco.     

小波变换和菲涅耳变换的多彩色图像加密

为了解决多彩色图像加密后,解密图像质量不佳、数据量大以及传输时速率慢的问题,采用了一种基于小波变换和菲涅耳变换的多彩色图像加密方法,加密过程中,利用小波变换的多级分解特性提取每幅彩色图像的低频分量,将低频分量分别重组为三元组图像（R,G和B）,并且依次将三元组图像（R,G和B）通过菲涅耳域中的衍射加密系统,对这3个三元组图像进一步加密,从而实现了多彩色图像的加密。结果表明,该方法不仅可以高质量地恢复原始彩色图像,而且可以同时对4幅彩色图像进行加密,提高了加密彩色图像的容量;原始图像经过小波变换,其数据量压缩到原来的1/4,有利于数据的传输和存储。该算法能够有效地同时对多幅色彩图像进行压缩和加密,不仅提高了解密图像的质量,并且具有较高的密钥敏感度和较好的鲁棒性。     

Two-dimensional phase unwrapping using wavelet transform

A method for solving the least squares two-dimensional phase unwrapping problem is presented. This technique is based on the multiresolution representation of a linear system using the discrete wavelet transform. By applying the wavelet transform to the original system, a better convergence condition of an equivalent new system can be achieved     

Edge detection of intestinal parasites in stool microscopic images using multi-scale wavelet transform

Biomedical image processing is experiencing a significant progress with many applications. However, automatic recognition of microscopic pathogens from their images remains a challenge that will allow clinical laboratories to increase both the speed of tests and the reliability of diagnoses. We present an algorithm for edge detection of parasites in microscopic images of stools, using the multi-scale wavelet transform. This method is an evolution of the Canny–Mallat detector which gives the possibility to vary the frequency of the analysis in order to find the outlines of the most significant edges. The various contours obtained are chained across the scales from the coarsest to the finest. Using this algorithm, we were able to correctly represent the contours of the features of parasites found in microscopic images. The results obtained were compared with those produced by classical edge detectors on the same images. It comes out from both subjective and objective quantitative performances evaluation that our detector, better than all others, can clearly mark the outlines of the structures of the pathogen on an image of stools.     

Improved transient signal detection using a wavepacket-baseddetector with an extended translation-invariant wavelet transform

This paper presents the theory of M-band, extended translation-invariant (ETI) wavelet transforms. The ETI generalizes the translation-invariant wavelet transform of Weiss (1933). It is shown that iteration of the ETI, in a tree structure, provides a signal decomposition into an orthonormal wavepacket basis. Other properties such as translation invariance and invertibility of the transform are proven. The theory is then applied to transient signal detection through development of a family of translation-invariant wavepacket-based detectors. This family of detectors provides improved the performance over previously defined wavepacket-based detectors. A performance analysis is conducted. ROC curves generated by Monte-Carlo simulation are presented, indicating the detector performance. The detector performance is demonstrated to be independent of the signal translation     

Image corner detection using topology learning

Image corner detection plays an important role in image analysis and recognition. This paper presents a novel corner detector based on the growing neural gas(GNG)network and this proposed detector is called GNG-C. With the GNG network, image topology information can be learned and used to implement corner detection. The GNG-C approach can be described as consisting of the following steps. First, a canny edge detector is used to acquire the contour information of the input image. This edge information is used to train a modified GNG network. A special stopping criterion is defined to terminate network learning. Second, vectors formed between network nodes and their neighbors are used to measure curvatures. Third, dynamic regions of support(ROS)are determined based on these curvatures. These ROS are used to suppress curvature noise. The curvature values of the nodes are then analyzed to estimate the candidate corners. Finally, the candidates are distilled by a non-maxima suppression process to obtain the final set of corners. Experiments on both artificial and real images show that the proposed corner detection method is feasible and effective.