Improved Feature Extraction by use of a Joint Wavelet Transform Correlator

A new joint wavelet transform correlation-based technique is proposed for feature extraction such as the detection of edges in an unknown input scene. We exploited a modified version of the Roberts and the Sobel wavelet filters as reference images for extracting the edges of an unknown input scene. The performance of the proposed technique with the aforementioned wavelet filters is evaluated and compared by use of numerical simulations. For noise-free input scenes the Roberts wavelet filter was found to yield a superior output compared with that of the Sobel wavelet filter. However, for noisy input scenes the Sobel wavelet filter was found to yield a better output compared with the Roberts wavelet filter.     

Real-time defect detection of steel wire rods using wavelet filters optimized by univariate dynamic encoding algorithm for searches

We propose a new defect detection algorithm for scale-covered steel wire rods. The algorithm incorporates an adaptive wavelet filter that is designed on the basis of lattice parameterization of orthogonal wavelet bases. This approach offers the opportunity to design orthogonal wavelet filters via optimization methods. To improve the performance and the flexibility of wavelet design, we propose the use of the undecimated discrete wavelet transform, and separate design of column and row wavelet filters but with a common cost function. The coefficients of the wavelet filters are optimized by the so-called univariate dynamic encoding algorithm for searches (uDEAS), which searches the minimum value of a cost function designed to maximize the energy difference between defects and background noise. Moreover, for improved detection accuracy, we propose an enhanced double-threshold method. Experimental results for steel wire rod surface images obtained from actual steel production lines show that the proposed algorithm is effective.     

Wavelet packet correlation methods in biometrics

We introduce wavelet packet correlation filter classifiers. Correlation filters are traditionally designed in the image domain by minimization of some criterion function of the image training set. Instead, we perform classification in wavelet spaces that have training set representations that provide better solutions to the optimization problem in the filter design. We propose a pruning algorithm to find these wavelet spaces by using a correlation energy cost function, and we describe a match score fusion algorithm for applying the filters trained across the packet tree. The proposed classification algorithm is suitable for any object-recognition task. We present results by implementing a biometric recognition system that uses the NIST 24 fingerprint database, and show that applying correlation filters in the wavelet domain results in considerable improvement of the standard correlation filter algorithm.     

Wavelet filter: pure-intensity spatial filters that implement wavelet transforms

Saloma [Opt. Lett. 20, 1943 (1995)] proposed the concept of mirrors with point-spread functions that exhibit wavelet-related characteristics. We propose novel filters with wavelet point-spread functions. The mirrors are suggested to reform not only the phases of optical waves, but also the filters for amplitude. The transmittance functions of the filters, which are real and positive under some conditions, are given. Optical wavelet transforms can easily be made with these filters, and computer simulations for edge and corner extractions are given.     

一种强噪声背景下的微弱信号检测的新方法

基于自适应滤波算法对小波滤波器的构造方法进行了研究,重点在于解决对强噪声环境下微弱信号提取所需要的小波变换的子带自适应滤波器的构造,把小波变换技术和自适应滤波技术应用于微弱信号的检测。提出基于自适应小波变换的心电信号的检测,利用小波变换的子带编码理论,通过在多个子带权值的自适应匹配,回复后拟合微弱信号。仿真结果表明,该方法可进一步改善信号的检测能力,在检测微弱信号的特征和改善信噪比方面是一种十分有效的方法。     

一种非一致小波基的超光谱图像压缩方法

传统的三维小波变换在不同维采用相同的小波基且频谱分辨能力也是相同的,这不能与超光谱图像的特性相匹配.本文提出了一种新的采用非一致小波基的去偶结构的三维小波变换的超光谱压缩方法.由于超光谱图像光谱维和空间维具有明显不同的特性而设计的,本文采用一种非一致小波基设计思路,对不同维使用不同的小波基并与去偶的小波变换结构结合在一起进行小波变换.为了获得符合不同维特性的最优的小波基组,本文通过评估指标来测试各种小波基组合的性能.实验结果表明,本文提出的方法能够明显提高超光谱图像的压缩比,且不需牺牲计算复杂度、可扩展性等额外代价.     

Texture Analysis for Flaw Detection in Ultrasonic Images

In this paper, we present two approaches for flaw detection in TOFD (Time of Flight Diffraction) images based on texture features. Texture is one of the most important features used in recognizing patterns in an image. The paper describes texture features by two methods: Multiresolution analysis such as wavelet transforms and Gabor filters bank. The two-dimensional wavelet transform is used to decompose the input image into a multiresolution framework. The textural statistical parameters are used to allow the choice of the decomposition channel. The Gabor filter is a Gaussian kernel function modulated by a sinusoidal plane wave. All Gabor filters can be generated from one mother wavelet by dilation and rotation. These filters represent an appropriate choice for tasks requiring simultaneous measurement in both space and frequency domains. The most relevant features are optimized by Principal Components Analysis (PCA) and used as input data on a Fuzzy C-Mean clustering classifier. We use two classes: ‘defects’ or ‘no defects’. The proposed approach is tested on the TOFD image achieved in an industrial field.     

二元正交小波滤波器的构造

本文提出了一种基于余弦函数的二元紧支撑正交小波滤波器的构造方法。这种构造方法简单易行,能够通过选取不同的参数而设计出不同的小波滤波器。最后,给出了算例。     

Optimal sub-band and strength selection for blind watermarking in wavelet domain

With increasing use of digital media, need for digital rights management has arisen. Watermarking is used to hide copyright protection information in the host medium. Hiding information to ensure digital right protection must ensure high imperceptibility and an acceptable level of robustness. In the watermark embedding, appropriate watermark strength and place selection in the host image is the most critical aspect of the whole process. Both watermark strength and place selection are considered as optimisation problems and are optimised using genetic algorithm (GA) and particle swarm optimisation (PSO). The watermark is embedded in the wavelet domain. With the proposed method optimal wavelet family, band, watermark strength and wavelet depth level are selected to ensure higher robustness and imperceptibility. The watermark is embedded in the selected bands of the wavelet packets. The band and wavelet depth is selected using GA and watermark strength is optimised using PSO method. The proposed method shows promising results against attacks on a variety of filters, i.e. low pass, high pass and median filters. Robustness results on JPEG compression and gaussian noise are also improved compared with the current approaches in practice.     

基于复小波变换与层间模型的图像去噪

很多小波去噪方法认为小波系数是相互独立的,然而大量实验表明实际图像的小波系数之间是有较强的依赖性。在本文中,我们将利用复小波变换的优势以及小波系数之间的依赖性,提出一种新的图像去噪方法。该方法先确定滤波器系数,再对复小波变换系数建模,并根据MAP准则给出系数的收缩方法进行去噪处理,最后作复小波逆变换。同时在变换的系数抽取之前估计系数的方差,可以使方差估计更准确。     

Optical implementation of a wavelet transform by the use of dynamic holographic recording in a photorefractive material

An optical system that employs holographic recording in a photorefractive material is proposed and experimentally demonstrated for the implementation of a wavelet transform of two-dimensional mages. A scaling operation, to derive the family of wavelet filters from a mother wavelet filter, is performed by the use of an optical feedback loop. The selection of a desired wavelet filter from the family and the correlation for a wavelet transformation are made by the use of a holographic recording in a photorefractive material. The principle of operation of the system relies on the frequency detuning introduced inside the loop and the subsequent variation in the holographic grating diffraction. Experimental results on wavelet-filter selection and wavelet transformation are presented. This nonlinear optical wavelet-transform system is advantageous for pattern recognition applications.     

An effective approach for wavelet lifting based on filter optimization and median operator

In JPEG2000, the Cohen–Daubechies–Feauveau (CDF) 9/7‐tap wavelet filter implemented by using the conventional lifting scheme has two problems. The first problem is that the filter coefficients are remaining complex; second, the conventional lifting scheme ignores image edges in the coding process. In this article, we propose an effective wavelet lifting scheme to solve these problems. For this purpose, we design the optimal 9/7‐tap wavelet filters in two steps. In the first step, we select the appropriate filter coefficients; in the second step, we employ a median operator to consider image edges. Experimental results from using the median lifting scheme and combining filter optimization and median lifting show that our proposed methods outperform the well‐known CDF 9/7‐tap wavelet filter of JPEG2000 on edge‐dominant images. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 359–366, 2010     

状态形态学滤波与EWT的故障特征提取方法研究

针对往复机械振动信号具有复杂非线性、非平稳等特性,使用一种基于小波框架的自适应经验小波变换和以集合角度处理信号的形态学滤波来进行往复机械故障特征提取。首先使用自适应经验小波变换通过构造尺度空间曲线对傅里叶频谱进行划分,构造合适的正交小波滤波器组以提取具有紧支撑傅里叶频谱的AM-FM成分;然后根据往复机械振动信号冲击性的特点,基于信号本身特性构造形态学结构元素,对提取出的模态进行状态自适应形态学滤波;最后使用多尺度模糊熵对模态进行定量分析并对故障进行识别。将该方法应用到实测数据中,实验结果验证了该方法的有效性,该方法可以准确对往复压缩机气阀故障进行识别。     

一种基于Gabor滤波器的超声图像边缘检测方法

针对检测超声图像的边缘问题，介绍了一种基于Gabor奇部滤波器进行边缘检测的综合方法。为了去除图像噪声，首先进行基于小波变换和中值滤波的降噪处理，然后利用高斯函数平滑图像。在边缘检测过程中，使用Gabor奇部滤波器检测边缘。最后，使用非最大值抑制得到最终结果。结果表明该方法是对超声图像进行边缘检测的一种有用方法。当然，该方法也具有普遍性，可以应用到其他图像。     

序列图像中运动目标检测

提出动态背景下序列图像中的运动目标检测算法。利用像素邻域的各向同性对图像进行归一化,消除亮度变化等因素的影响;利用光流信息并结合小波变换由粗及精计算速度场来配准图像;用当前帧作参考图像,通过时域积分校正背景图像。当前帧与校正后背景图像作差得到差分图像。假设该差分图像中噪声分布为高斯分布,由高斯分布的3σ特性滤除差分图像中的噪声,则粗定位出目标;最后以聚类方法确定运动目标区域。分别对200帧可见光和200帧红外图像序列进行实验,检测率分别为95%和94%。     

Multiresolution correlator analysis and filter design

The concept of multiresolution optical correlators is formally introduced. A mathematical analysis is performed for a generalized multiresolution correlator that emphasizes the roles of both input and filter spatial light modulator resolutions. Conditions are derived for overlapping and nonoverlapping correlation orders. A simulation is performed in which it is shown that the predicted performance of composite binary-phase-only filters designed by the conventional design procedure is different from the actual performance when they are implemented in a real optical correlator. The training of filters on multiresolution approximations of high-resolution discrete Fourier transforms generated by multiresolution wavelet analysis (MWA) techniques is proposed. An analysis is performed that shows that training on MWA approximations results in filters whose performance is the same in a real correlator as that predicted by the design procedure. This analysis is confirmed by simulation. Further simulations show that the performance of reduced-resolution filters designed by MWA techniques is markedly superior to the performance of those designed by conventional means. Finally, an analysis is performed that explains why the ratio of zero- to higher-order correlation peak intensities is much greater for the former than the latter.     

空频域量化误差最小的光学小波滤波器设计

通过理论分析和仿真计算,研究利用不同小波基构造的光学小波滤波器的量化误差,分析交换分解和重构滤波器对光学小波滤波器量化误差的影响.实验结果表明不同小波基的量化误差通常是不同的,具有最小空、频域量化误差的小波基是交换分解和重构滤波器后的5/3 小波.根据光学小波滤波器的空、频域量化误差最小原则,基于提升算法,构造出空、频域量化误差最小的最优小波.     

Comparison between wavelet-based and Fourier-based multicarrier UWB systems

The use of wavelet transform in multicarrier ultra wideband (UWB) systems is analysed and the results are compared with Fourier-based multicarrier UWB systems. It is well known that convolution in time domain is equivalent to multiplication in frequency domain; however, there is no closed-form expression in the literature for convolution's counterpart in the wavelet domain. A formula is derived to represent convolution's counterpart in the wavelet domain. Furthermore, the effects of choice and type of wavelet filters and different decomposition levels are investigated. Finally, a performance comparison of both techniques for IEEE 802.15.3a channel models using IEEE802.15.3a multiband-orthogonal frequency division multiplexing UWB specifications is provided.     

A hybrid contextual compression technique using wavelet and contourlet transforms with PSO optimized prediction

Contextual compression is an essential part of any medical image compression since it facilitates no loss of diagnostic information. Although there are many techniques available for contextual image compression still there is a need for developing an efficient and optimized technique which would produce good quality images at lower bit rates. This article presents an efficient contextual compression algorithm using wavelet and contourlet transforms to capture the fine details of the image, along with directional information to produce good quality at high Compression Ratio (CR). The 2D discrete wavelet transform, which uses the simplest Daubechies wavelets, db1, or haar wavelet, is chosen and used to get the subband coefficients. The approximate coefficients of the higher subbands undergo contourlet transform employing length N ladder filters for capturing the directional information of the subbands at different scale and orientations. An optimized approach is used for predicting the quantized and the normalized subband coefficients resulting in improved compression performance. The proposed contextual compression approach was evaluated for its performance in terms of CR, Peak Signal to Noise Ratio, Feature SIMilarity index, Structure SIMilarity Index, and Universal quality (Q) after reconstruction. The results clarify the efficiency of the proposed method over other compression techniques.     

Gearbox Fault Detection Using Real Coded Genetic Algorithm and Novel Shock Response Spectrum Features Extraction

This paper presents an intelligent fault detection method for gearbox. The method uses band-pass and wavelet filtering with real coded genetic algorithm (RCGA) and shock response spectrum (SRS) for features extraction. Vibration data acquired from gearbox are adaptively filtered through a band-pass and wavelet filters optimized by the RCGA. The filtering process unveils the fault pulses buried under huge background noise. Shock response spectrum is used to calculate the amount of shock produced by these pulses over a frequency band of interest for features extraction. The proposed method is a combination of intelligent and conventional search techniques, which shows a high performance and accurate fault detection results. The effectiveness, feasibility, and robustness of the proposed method are demonstrated on experimental data. The RCGA has successfully achieved an average speed up factor of 74 %, as compared to conventional genetic algorithms (GA) while preserving the quality of results.