一种新的Harris多尺度角点检测

Harris角点检测是一种经典的角点检测算法,但不具有尺度变化特性。该文把多分辨分析的思想引入到该算法中,构造了基于小波变换的灰度强度变化公式,并得到了具有尺度变换特性的自相关矩阵,从而构建了一种新的基于小波变换的Harris多尺度角点检测算法。这样,使得新的角点检测可以在不同的尺度下获取角点,并克服了单一尺度的Harris角点检测可能存在的角点信息丢失、角点位置偏移和易受噪而提取出伪角点等问题。通过对比实验,新算法明显地提高了图像角点检测性能。     

使用小波变换的图象边缘检测算法

将小波变换理论应用于图象边缘提取，提出了一种新的图象边缘检测算法。一种相对于（ｃ０，ｃ３）＝（０．０５，０．０５）的对称尺度函数被用于二进小波变换，得到原图象的一个多分辨率表达式，再提取图象的边缘特征。     

尺度相互作用墨西哥帽小波提取图像特征点

图像特征点是图像的重要局部特征,它是图像理解和模式识别中的重要信息,图像特征点提取技术也是图像处理的有力工具之一。提出了一种在尺度相互作用模型下利用墨西哥帽小波来提取图像特征点方法,该方法对于经过旋转、亮度、模糊以及噪声处理后的失真图像仍能提取出相对位置和数量都较为一致的特征点。而针对尺度相互作用的墨西哥帽小波提取不同尺度的图像时的特征点相对位置不一致的问题,提出了在墨西哥帽小波中加入尺度因子的方法,通过仿真实验验证了算法的正确性。     

使用小波变换的图象边缘检测算法

将小波变换理论应用于图象边缘提取,提出了一种新的图象边缘检测算法。一种相对于(c0,c3)=(0.05,0.05)的对称尺度函数被用于二进小波变换,得到原图象的一个多分辨率表达式,再提取图象的边缘特征。实验结果表明,本方法是解决图象边缘提取的一个十分有效的模型。     

反对称双正交小波应用于多尺度边缘提取的研究

本文对反对称双正交小波所具有的多尺度边缘提取能力进行了理论分析，提出了一种反对称双正交小波变换域内的多尺度边缘提取算法，并通过实验进行了验证。该结果为在基于小波变换的压缩数据域内利用边缘信息实现图像检索提供了依据。     

基于小波变换的红外图象多尺度边缘检测

本文讨论了小波变换及其应用于多尺度图象边缘检测的原理，对一幅红外图象给出其多尺度边缘检测的计算机仿真结果，而且与传统的边缘检测方法进行比较，从而得出基于小波变换的多尺度边缘检测是一种较好的方法     

基于小波变换的密封件边缘检测

针对密封件内、外径的检测要求，提出了基于B样条小波变换的多尺度阈值图像边缘检测新方法。该方法采用小波的多分辨率分析，先计算小波变换模的极大值，然后，基于多尺度阈值消噪处理，进而检测出信号的奇异点位置，达到密封件的边缘检测目的。实验结果证明，该方法不仅能够较为准确地检测出有用的边缘信息，且具有一定的抗噪能力，是一种有效的边缘检测算法。     

基于小波变换的音频信号基频提取

通过对小波变换表征信号突变原理的研究，给出了基于小波变换的音频信号基频提取的原理和算法。在算法的实现过程中，利用信号在时域和频域的对应关系提出了一种动态确定小波变换最佳尺度的方法，克服了固定变换尺度在检测基频变化范围大的声音信号时的不足，并将数学统计方法引入到极大值点的提取。在音频信号的实时基频检测实验中，该算法较准确地定位最佳尺度和极大值点，取得了较好的基频提取结果。     

基本电子电路

TN7012006030542织物表面折皱的小波分析与自组织神经网络等级评定/杨晓波,黄秀宝(浙江财经学院信息学院)//中国图象图形学报.―2005,10(4)2005,10(4).―473～478.为了提取较为精细的图像信息,引入了多尺度2维小波分析织物的表面折皱。织物图像首先经过高斯滤波,再利用小波变换     

基于小波变换的故障信号分析与检测

讨论了小波变换及其基本性质，探讨基于小波变换模最大值沿尺度演变的信号突变检测的基本原理与方法，在不同尺度上分析和处理信号的各种频率分量，使信号的奇点，突变点放大，提高信号的分辨率，信噪比。研究小波分析在机械工程信号处理中的应用，提出通过二进小波变换检测信号奇异点的实用技术。利用介绍的模极大值多尺度小波变换的方法，较有效地检测出滚动轴承故障发生的起始点，对在线故障诊断进行了有益的探索。     

Multiscale corner detection by using wavelet transform

A multiscale corner detection algorithm based on the wavelet transform of contour orientation is proposed. It can utilize both the information of the local extrema and modulus of transform results to detect corners and arcs effectively. The ramp-width of contour orientation profile, which can be computed using the transformed modulus of two scales, reveals the difference between corner and arc and is utilized in the determination of corner points. The experimental results have shown that the detector is more effective than both the single- and multiple-scale detectors. They also demonstrate that the detector is insensitive to boundary noise. In addition, the proposed method is more efficient than the other multiscale corner detector because it operates on fewer number of scales, which can be implemented by a fast transform algorithm.     

A Shearlet Approach to Edge Analysis and Detection

It is well known that the wavelet transform provides a very effective framework for analysis of multiscale edges. In this paper, we propose a novel approach based on the shearlet transform: a multiscale directional transform with a greater ability to localize distributed discontinuities such as edges. Indeed, unlike traditional wavelets, shearlets are theoretically optimal in representing images with edges and, in particular, have the ability to fully capture directional and other geometrical features. Numerical examples demonstrate that the shearlet approach is highly effective at detecting both the location and orientation of edges, and outperforms methods based on wavelets as well as other standard methods. Furthermore, the shearlet approach is useful to design simple and effective algorithms for the detection of corners and junctions.     

尺度空间检测边缘特征的进化分析

多尺度分析中的一个重要问题是检测特征的尺度空间进化规律。本文以2-D高斯函数作为核函数,明确给出了角点图像的尺度空间检测边缘是一个由角边界的斜率和分析尺度共同决定的椭圆。以此为基础,讨论了尺度因子对相邻角点检测边缘的同化效应,给出了角最小分辨率与分析尺度的关系不等式。本文的结果对多尺度分析具有普遍性参考意义。     

一种小波变换的多尺度方法及其在波达时间精确估测中的应用

本文提出了一种雷达回波到达时间精确估测的多尺度方法．该方法只需对接收信号计算三尺度的小波变换．和对所检测到候选回波的两尺度小波变换。这种方法可采用Mallat算法．故其计算效率是很高的。实验结果也表明该方法不仅能精确检测雷选回波并测量其波选时刻．剔除多径干扰，而且还具有很强的噪声抑制能力。     

Vehicle detection using normalized color and edge map.

This paper presents a novel vehicle detection approach for detecting vehicles from static images using color and edges. Different from traditional methods, which use motion features to detect vehicles, this method introduces a new color transform model to find important "vehicle color" for quickly locating possible vehicle candidates. Since vehicles have various colors under different weather and lighting conditions, seldom works were proposed for the detection of vehicles using colors. The proposed new color transform model has excellent capabilities to identify vehicle pixels from background, even though the pixels are lighted under varying illuminations. After finding possible vehicle candidates, three important features, including corners, edge maps, and coefficients of wavelet transforms, are used for constructing a cascade multichannel classifier. According to this classifier, an effective scanning can be performed to verify all possible candidates quickly. The scanning process can be quickly achieved because most background pixels are eliminated in advance by the color feature. Experimental results show that the integration of global color features and local edge features is powerful in the detection of vehicles. The average accuracy rate of vehicle detection is 94.9%.     

Multiscale penalized weighted least-squares sinogram restoration for low-dose x-ray computed tomography

In this paper, we propose a novel multiscale penalized weighted least-squares (PWLS) method for restoration of low-dose computed tomography (CT) sinogram. The method utilizes wavelet transform for the multiscale or multiresolution analysis on the sinogram. Specifically, the Mallat-Zhong's wavelet transform is applied to decompose the sinogram to different resolution levels. At each decomposed resolution level, a PWLS criterion is applied to restore the noise-contaminated wavelet coefficients, where the penalty is adaptive to each resolution scale and the weight is updated by an exponential relationship between the data variance and mean at each scale and location. The proposed PWLS method is based on the observations that 1) noise in the CT sinogram after logarithm transform and calibration can be modeled as signal-dependent variables and the sample variance depends on the sample mean by an exponential relationship; and 2) noise reduction can be more effective when it is adaptive to different resolution levels. The effectiveness of the proposed multiscale PWLS method is validated by both computer simulations and experimental studies. The gain by multiscale approach over single scale means is quantified by noise-resolution tradeoff measures.     

回波到达时间精确估测的多尺度方法

本文提出了一种雷达回波到达时间精确估测的多尺度方法。通过在不同尺度上对回波特性进行综合分析,该方法不仅能剔除多径干扰,正确检测雷达回波,精确测量其波达时刻,同时还具有很强的抑制噪声的能力。由于该方法只需对接收信号计算三尺度的小波变换和对所检测到的候选波作两尺度小波变换且能采用Mallat算法,故其计算效率很高。实验结果验证了本文方法的有效性。     

基于多尺度屋顶状边缘的目标检测

本文利用小波为工具提出了在多尺度检测图象中屋顶边缘的方法，导出了该处屋顶边缘的最大曲率及其方向的计算公式；并将其结合人脸的特点用以在较复杂背景的图象中确定人脸及其器官（如眼睛）的位置和进行初步的分割，此方法在实验中取得了良好的效果。     

一种基于Ridgelet变换的遥感图像融合方法

Ridgelet变换是继经典的小波变换之后提出来的一种新型图像多尺度几何分析工具.针对不同波段的远程遥感图像提出了一种基于新型正交Ridgelet变换的遥感图像融合方法.该算法基于新型的可逆离散脊波多尺度变换,通过客观评估融合性能说明该方法优于其他三种典型融合方法,尤其是优于各种基于小波变换的图像融合方法,仿真实验证明该方法融合效果良好.     

Wavelets, ridgelets, and curvelets for Poisson noise removal.

In order to denoise Poisson count data, we introduce a variance stabilizing transform (VST) applied on a filtered discrete Poisson process, yielding a near Gaussian process with asymptotic constant variance. This new transform, which can be deemed as an extension of the Anscombe transform to filtered data, is simple, fast, and efficient in (very) low-count situations. We combine this VST with the filter banks of wavelets, ridgelets and curvelets, leading to multiscale VSTs (MS-VSTs) and nonlinear decomposition schemes. By doing so, the noise-contaminated coefficients of these MS-VST-modified transforms are asymptotically normally distributed with known variances. A classical hypothesis-testing framework is adopted to detect the significant coefficients, and a sparsity-driven iterative scheme reconstructs properly the final estimate. A range of examples show the power of this MS-VST approach for recovering important structures of various morphologies in (very) low-count images. These results also demonstrate that the MS-VST approach is competitive relative to many existing denoising methods.