小波变换在电路故障诊断中的应用

介绍了利用小波变换来检测信号的奇异性特征的原理，对信号的奇异性进行了定义，并将其用于电路故障诊断中，解决了传统故障诊断方法中对故障存在的知识获取“瓶颈”问题。实验证明提出的方法取得了较好的应用效果。     

Iris detection using intensity and edge information

In this paper we propose a new algorithm to detect the irises of both eyes from a face image. The algorithm first detects the face region in the image and then extracts intensity valleys from the face region. Next, the algorithm extracts iris candidates from the valleys using the feature template of Lin and Wu (IEEE Trans. Image Process. 8 (6) (1999) 834) and the separability filter of Fukui and Yamaguchi (Trans. IEICE Japan J80-D-II (8) (1997) 2170). Finally, using the costs for pairs of iris candidates proposed in this paper, the algorithm selects a pair of iris candidates corresponding to the irises. The costs are computed by using Hough transform, separability filter and template matching. As the results of the experiments, the iris detection rate of the proposed algorithm was 95.3% for 150 face images of 15 persons without spectacles in the database of University of Bern and 96.8% for 63 images of 21 persons without spectacles in the AR database.     

一种新颖的图像水印嵌入方法

An novel embedding method for image watermark based on wavelet transform is proposed. With this method ,the value of each pels is modified directly,so that a bit of watermark information is embedded in every pel-s. Before emeded,the original image is decomposed by wavelet transform,and then,a correlation mask for every imagepels is confirmed by the characteristic and tree structure ralation of the wavelet coefficients. At last, the modified val-ue of every pels is controlled by its corresponding mask for watermark information embedded. The experimental re-sults show that the embedded digital watermarks with this proposed method are invisible and robust enough againstthe commonly used image processing techniques.     

基于JPEG的图像检索综述

基于内容的检索是近年来的研究热点之一，现在已有许多基于象素域的图像检索技术，目前数据压缩也已成为多媒体应用的标准模式，静态图像压缩主要采用JPEG技术，研究基于传统JPEG和JPEG2000的图像检索方法成为必然。本文综述近年来出现的基于JPEG核心算法离散余弦变换和JPEG2000核心算法离散小波变换的图像检索技术。     

基于子波变换的一种信号除噪新算法研究

针对Donoho去噪算法中阈值难以确定，通过分析白噪声的能量分布在于波变换域随尺度变化的规律，提出了一种采用子波变换来消除信号噪声的新算法。通过对含噪信号的求导运算实现信号与噪声在于波变换域约分离，以达到消除噪声的目的，算法的特点在于能够自动给出信号中噪声的阈值。经过测试，该算法在消除信号中的白噪声时，能够取得比较理想的效果。     

精细可伸缩视频编码中的增强层编码方法研究

提出了一种新的子带编码方法来对精细可伸缩视频编码中的增强层进行编码。在这种编码方法中,首先通过重排DCT残差系数形成子带,然后利用同一子带相邻系数的相关性来消除数据冗余。实验结果表明文章提出的增强层编码方法比原来的FGS增强层编码方法具有更高的编码效率。     

Texture classification using the support vector machines

In recent years, support vector machines (SVMs) have demonstrated excellent performance in a variety of pattern recognition problems. In this paper, we apply SVMs for texture classification, using translation-invariant features generated from the discrete wavelet frame transform. To alleviate the problem of selecting the right kernel parameter in the SVM, we use a fusion scheme based on multiple SVMs, each with a different setting of the kernel parameter. Compared to the traditional Bayes classifier and the learning vector quantization algorithm, SVMs, and, in particular, the fused output from multiple SVMs, produce more accurate classification results on the Brodatz texture album.     

Application of InP Neural Network to ECG BeatClassification

This paper presents an application of a hybrid neural network structure to the classification of the electrocardiogram (ECG) beats. Three different feature extraction methods are comparatively examined: discrete cosine transform, wavelet transform and a direct method. Classification performances, training times and the numbers of nodes of Kohonen network, Restricted Coulomb Energy (RCE) network and the hybrid neural network are presented. To increase the classification performance and to decrease the number of nodes, the hybrid neural network is trained by Genetic Algorithms (GAs). Ten types of ECG beats obtained from the MIT-BIH database and from a real-time ECG measurement system are classified with a success of 98% by using the hybrid neural network structure and discrete cosine transform together.     

A Generative Probabilistic Oriented Wavelet Model for Texture Segmentation

This Letter addresses image segmentation via a generative model approach. A Bayesian network (BNT) in the space of dyadic wavelet transform coefficients is introduced to model texture images. The model is similar to a Hidden Markov model (HMM), but with non-stationary transitive conditional probability distributions. It is composed of discrete hidden variables and observable Gaussian outputs for wavelet coefficients. In particular, the Gabor wavelet transform is considered. The introduced model is compared with the simplest joint Gaussian probabilistic model for Gabor wavelet coefficients for several textures from the Brodatz album [1]. The comparison is based on cross-validation and includes probabilistic model ensembles instead of single models. In addition, the robustness of the models to cope with additive Gaussian noise is investigated. We further study the feasibility of the introduced generative model for image segmentation in the novelty detection framework [2]. Two examples are considered: (i) sea surface pollution detection from intensity images and (ii) image segmentation of the still images with varying illumination across the scene.     

一种基于小波多分辨率的数字水印方法

基于小波多分辨率的分解重构，提出了一种数字水印方法。该方法是将一幅二值化图像作为水印，不借助原始图像，能方便、可靠地检测出水印。实验表明嵌入水印后的图像及检测水印图像视觉失真率低，嵌入水印有很好的鲁棒性。