Variable lighting face recognition using discrete wavelet transform

This paper presents a new discrete wavelet transform (DWT) based illumination normalization approach for face recognition under varying lighting conditions. Our method consists of three steps. Firstly, DWT-based denoising technique is employed to detect the illumination discontinuities in the detail subbands. And the detail coefficients are updated with using the obtained discontinuity information. Secondly, a smooth version of the input image is obtained by applying the inverse DWT on the updated wavelet coefficients. Finally, multi-scale reflectance model is presented to extract the illumination invariant features. The merit of the proposed method is it can preserve the illumination discontinuities when smoothing image. Thus it can reduce the halo artifacts in the normalized images. Moreover, only one parameter involved and the parameter selection process is simple and computationally fast. Experiments are carried out upon the Yale B and CMU PIE face databases, and the results demonstrate the proposed method can achieve satisfactory recognition rates under varying illumination conditions.     

使用离散小波矩和遗传算法的目标识别

提出了一种连续小波矩的离散化算法,用于目标识别中的特征提取。因为可提取的小波矩数量较大,为简化分类器的设计,进一步提出了利用遗传算法（GA）进行自动的特征优化选择。仿真实验表明了离散小波矩的旋转不变性。基于瑞士联邦理工学院计算机视觉实验室的Obj_DB物体数据库,进一步测试了识别性能。结果表明,方法对任意选择的12个物体达到了100%识别率,优于无特征选择的方法和Hu矩特征方法。     

Face recognition using fuzzy Integral and wavelet decomposition method

In this paper, we develop a method for recognizing face images by combining wavelet decomposition, Fisherface method, and fuzzy integral. The proposed approach is comprised of four main stages. The first stage uses the wavelet decomposition that helps extract intrinsic features of face images. As a result of this decomposition, we obtain four subimages (namely approximation, horizontal, vertical, and diagonal detailed images). The second stage of the approach concerns the application of the Fisherface method to these four decompositions. The choice of the Fisherface method in this setting is motivated by its insensitivity to large variation in light direction, face pose, and facial expression. The two last phases are concerned with the aggregation of the individual classifiers by means of the fuzzy integral. Both Sugeno and Choquet type of fuzzy integral are considered as the aggregation method. In the experiments we use n-fold cross-validation to assure high consistency of the produced classification outcomes. The experimental results obtained for the Chungbuk National University (CNU) and Yale University face databases reveal that the approach presented in this paper yields better classification performance in comparison to the results obtained by other classifiers.     

应用复小波和独立成分分析的人脸识别

结合双树复小波变换（DT-CWT）和独立成分分析（ICA）提出了一种人脸识别新方法。该方法首先应用双树复小波变换提取图像的特征向量,接着通过主成分分析（PCA）降低特征向量的维数,在此基础上应用独立成分分析提取统计上独立的特征向量,然后基于相关系数的分类器对特征向量进行分类。双树复小波变换具有方向与尺度选择性,并能有效的保持图像的频域信息,其与独立成分分析相结合提取的特征具有良好的分类性能。在ORL和AR人脸图像数据库上进行算法验证的结果表明该方法的有效性。     

融合小波和自适应类增广PCA的人脸识别

提出了融合小波变换和自适应类增广PCA（CAPCA）的人脸识别算法。用离散小波变换对人脸图像进行压缩,提取人脸的低频分量,再利用自适应的类增广PCA方法对小波变换后的人脸低频分量进行特征提取,从而达到进一步降维的目的。不同于类增广PCA,该方法不需要构建样本的类间信息,使用起来更加灵活,又由于小波变换对图像的预处理,算法的识别率和耗时也得到了进一步的优化。Yale和FERET库上的实验表明了该算法的有效性。     

Continuous and discrete Mexican hat wavelet transforms on manifolds

This paper systematically studies the well-known Mexican hat wavelet (MHW) on manifold geometry, including its derivation, properties, transforms, and applications. The MHW is rigorously derived from the heat kernel by taking the negative first-order derivative with respect to time. As a solution to the heat equation, it has a clear initial condition: the Laplace–Beltrami operator. Following a popular methodology in mathematics, we analyze the MHW and its transforms from a Fourier perspective. By formulating Fourier transforms of bivariate kernels and convolutions, we obtain its explicit expression in the Fourier domain, which is a scaled differential operator continuously dilated via heat diffusion. The MHW is localized in both space and frequency, which enables space-frequency analysis of input functions. We defined its continuous and discrete transforms as convolutions of bivariate kernels, and propose a fast method to compute convolutions by Fourier transform. To broaden its application scope, we apply the MHW to graphics problems of feature detection and geometry processing.     

Non-parametric linear time-invariant system identification by discrete wavelet transforms

We describe the use of the discrete wavelet transform (DWT) for non-parametric linear time-invariant system identification. Identification is achieved by using a test excitation to the system under test (SUT) that also acts as the analyzing function for the DWT of the SUT's output, so as to recover the impulse response. The method uses as excitation any signal that gives an orthogonal inner product in the DWT at some step size (that cannot be 1). We favor wavelet scaling coefficients as excitations, with a step size of 2. However, the system impulse or frequency response can then only be estimated at half the available number of points of the sampled output sequence, introducing a multirate problem that means we have to ‘oversample’ the SUT output. The method has several advantages over existing techniques, e.g., it uses a simple, easy to generate excitation, and avoids the singularity problems and the (unbounded) accumulation of round-off errors that can occur with standard techniques. In extensive simulations, identification of a variety of finite and infinite impulse response systems is shown to be considerably better than with conventional system identification methods.     

Potential functions-based wavelet transforms in pattern recognition problems

The problem of finding a given function in the composition of another function for the one-dimensional case, as well as the problem of finding particular fragments in the composition of a given surface, are considered. The application of wavelet transforms in solving the posed problems is shown by particular examples.     

Speckle suppression using recursive wavelet transforms

An effective algorithm for digital image noise smoothing using wavelet transform techniques is presented in this paper. This algorithm is more powerful when compared to other existing filtering algorithms in terms of speckle suppression for synthetic aperture radar images where the presence of speckle makes the ratio of standard deviation to mean (STM) very high. Examples show that the original STM of about 0.30 (equivalent to three-look images) can be reduced to 0.05-0.03 (equivalent to more than 100-look images), with a possible small sacrifice of losing some details and narrow edges. The quantitative analysis is carried out and compared with the results of some existing filtering algorithms including median, K nearest neighbour averaging, Lee's multiplicative and Crimmins' geometric filters, showing that imagery filtered by the wavelet transform is the smoothest.     

一种基于冗余小波变换的快速运动估计算法

由于冗余小波变换克服了离散小波变换的移变性,在冗余小波域进行运动估计可以达到很好的效果,其缺点是算法的时间复杂度过高。在分析视频序列的运动特性和已有的运动估计算法的基础上,提出一种快速的基于冗余小波变换的运动估计算法。在冗余小波域提取潜在运动区域并对图像块的运动剧烈程度进行划分,在此基础上对不同类型的图像块采取不同的搜索策略,从而减少了运动估计中不必要的搜索。实验结果表明,该方法较经典的基于冗余小波变换的运动估计算法在时间复杂度方面具有优势。     

二次离散小波变换的语音增强方法

提出了一种基于二次离散小波变换（DWT）的语音增强算法。该算法首先对带噪语音信号进行离散小波变换,提取离散细节信号,并对其进行第二次离散小波变换。再按照不同的规则选取阈值,对信号进行去噪处理。最后再对出来后的语音信号进行合并。对比实验结果表明,该方法具有良好的消除噪声的效果,提高了语音的清晰度和可懂度。     

基于小波和最近邻凸包分类器的人脸识别

本文提出一种新型的人脸识别方法.该方法首先通过二维小波变换提取人脸图像的低频特征,然后采用最近邻凸包分类器对该特征进行分类.二维小波变换是提取图像特征的有效方法之一,在保留原始图像的主要特征的同时,还能够有效降低图像维数;最近邻凸包分类器是一种以测试样本点到各类别训练样本凸包的距离作为相似性度量的分类算法.本文将这两项技术相结合在ORL人脸识别数据库上取得了良好的实验效果.     

冗余离散小波变换立体视差估计及DSP实现

立体图像技术将是未来多媒体发展的重点方向,其中视差估计是立体图像处理的关键,针对目前视差估计方法的不足,提出了一种基于冗余离散小波变换的视差估计算法。首先对参考图像进行冗余小波变换,提取特征点,然后根据特征点在目标图像进行视差估计。实验表明该算法能有效获得视差矢量,视差匹配后能得到良好的重建图像。同时在TI公司的多媒体器件DM642上进行了实验。结果表明,提出的设计方案具有实时可行性,并较有效地减少了运算复杂性。     

Adaptive edge-preserving image denoising using wavelet transforms

Image denoising is a relevant issue found in diverse image processing and computer vision problems. It is a challenge to preserve important features, such as edges, corners and other sharp structures, during the denoising process. Wavelet transforms have been widely used for image denoising since they provide a suitable basis for separating noisy signal from the image signal. This paper describes a novel image denoising method based on wavelet transforms to preserve edges. The decomposition is performed by dividing the image into a set of blocks and transforming the data into the wavelet domain. An adaptive thresholding scheme based on edge strength is used to effectively reduce noise while preserving important features of the original image. Experimental results, compared to other approaches, demonstrate that the proposed method is suitable for different classes of images contaminated by Gaussian noise.     

Evaluation of discrete transforms for use in digital speech recognition

Traditionally FFT (fast Fourier transform) has been utilized in recognition algorithms involving speech. Other discrete transforms such as Walsh-Hadamard transform (WHT) and rapid transform (RT) can play equally important roles in the recognition process as they have advantages in implementation and hardware realization. The capability of these transforms in recognizing phonemes based on training matrices and various matching criteria is investigated. The speech data base consists of ten sentences spoken by ten different speakers (all male). For recognition purposes the speech is sectioned into 10 ms intervals and is sampled at 20 KHz. Training matrices for all the three transforms are developed. Test matrices in the transform domain are compared with the prototypes based on these criteria which led to the decision process. WHT and RT appear to offer promise and potential compared to FFT as the former are easier to implement and as the yield recognition results comparable to those of the FFT. Other distance measures and recognition schemes are proposed for improving the classification accuracy.     

基于光照变换的Gabor小波人脸识别

光照变化易使人脸图像的灰度分布不均,造成局部对比度差别较大,会引起人脸识别正确率下降。为此在同态滤波的基础上,改变滤波函数,提出了高斯滤波的人脸识别方法,接着对滤波后的图像直方图均衡化,来增加图像的灰度动态范围,然后对人脸图像提取Gabor小波特征,最后利用最近邻法识别人脸图像。在光照变换明显的Yale B和CMU PIE数据库识别效果最好,降低了人脸图像的特征维数,缩短了特征提取时间,有效地提高了人脸识别率。     

基于二元树复小波特征表示的人脸识别方法

二元树复小波变换（DTCWT）具备近似平移不变、多方向选择、完全重构和高效计算等优点，适合于人脸特征提取。提出了一种新的基于二元树复小波变换的人脸特征表示方法，用二维DTCWT提取了人脸图像不同尺度、位置和方向的局部特征，并用多尺度多方向的信息生成DTCWT人脸特征图。实验证明了DTCWT人脸特征表示方法提取了最具可判别性的人脸特征，获取了高识别率和泛化能力，优于其他特征表示方法。     

Classification of melanoma using tree structured wavelet transforms

This paper presents a wavelet transform based tree structure model developed and evaluated for the classification of skin lesion images into melanoma and dysplastic nevus. The tree structure model utilizes a semantic representation of the spatial-frequency information contained in the skin lesion images including textural information. Results show that the presented method is effective in discriminating melanoma from dysplastic nevus. The results are also compared with those obtained using another method of developing tree structures utilizing the maximum channel energy criteria with a fixed energy ratio threshold.     

基于二进小波变换和仿生模式识别的人脸识别

针对人脸图像易受光线和表情影响的特点,提出了一种基于二进小波变换和仿生模式识别的人脸识别方法。应用样条二进小波对人脸图像进行处理,对得到的细节子图进行融合。在FFT和PCA处理与降维后,用仿生模式识别进行学习和识别。实验结果表明,该方法比传统方法具有更高的识别率。