二进离散小波神经网络在传感器逆向建模中的应用

给出了快速收敛的离散二进小渡神经网络的初始化．构造和权值确定的详细方法。并将这类小波神经网络应用于传感器的非线性校正，并给出了仿真实验结果。相对使用随机贪心算法训练的神经网络，快速收敛小波神经网络利用离散二进小波变换的便利，采用启发式的构造算法；具有构造过程复杂度低，构造完成后高度接近目标模型，训练次数少，并可有效避免陷入局部极小点的优点。有效解决了小波神经网络尺度和平移系数在训练时需对小波函数进行求导而影响网络收敛速度的问题。     

Key Points-based Indexing for Pre-attentive Similarities: The KIWI System

In this paper, we introduce the general architecture of an image-search engine based on pre-attentive similarities. Local features are computed in key points to represent local properties of the images. The location of key points, where local features are computed, is discussed. We present two new key point detectors designed for image retrieval, both based on multi-resolution: the contrast-based point detector, and the wavelet-based point detector. Four different local features are used in our system: differential invariants, texture, shape and colour. The local information computed in each key point is stored in 2D histograms to allow fast querying. We study the choice of the key points detector depending on the feature used, for different test sets. The Harris corner detector is used for benchmarking. Uniformly distributed points are also used, and we conclude for which applications they are effective. Finally, we show that point detector and feature efficiency depend upon the test set studied.     

Continuous wavelet based linear time-varying system identification

A systematic framework is developed to address the parametric linear time-varying system identification problem, using the continuous wavelet transform (CWT). The system is modeled by a differential equation with unknown parameters and identified via the time-frequency representation, the ratio of the CWTs of the output and the input. The efficient execution of this system identification algorithm requires selecting appropriate scales from the wavelet transform. Scale selection can be formulated as an optimization problem: find a set of scales that contains the most information about the system’s dynamics and has high signal to noise ratio. In addition, selecting scales that have a minimum amount of redundant information is desirable. Three candidate selection metrics are presented that address these criteria and are based on an analytic investigation of the wavelet transform’s probabilistic structure. Finally, a non-linear least squares algorithm, coupled with a scale selection algorithm, is presented to identify the system model. Simulations and experiments verify this algorithm’s capability of tracking different types of model variation.     

Non-separable lifting scheme with adaptive update step for still and stereo image coding

Many existing works related to lossy-to-lossless multiresolution image compression are based on the lifting concept. It is worth noting that a separable lifting scheme may not appear very efficient to cope with the 2D characteristics of edges which are neither horizontal nor vertical. In this paper, we propose to use 2D non-separable lifting schemes that still enable progressive reconstruction and exact decoding of images. Their relevant advantage is to yield a tractable optimization of all the involved decomposition operators. More precisely, we design the prediction operators by minimizing the variance of the detail coefficients. Concerning the update filters, we propose a new optimization criterion which aims at reducing the inherent aliasing artifacts. A theoretical analysis of the proposed method is conducted in terms of the adaptation criterion considered in the optimization of the update filter. Simulations carried out on still images and residual ones generated from stereo pairs show the benefits which can be drawn from the proposed optimization of the lifting operators.     

一种复合型自适应数字水印算法

提出一种同时嵌入鲁棒性水印和易损性水印的复合型数字水印算法。其中鲁棒性水印的算法,利用图像小波系数的零树结构,基于零树小波编码,构造出重要系数小波树,将水印信息自适应地以不同强度嵌入载体图像的合适位置。实验结果表明,本算法在用于版权认证的同时还能对篡改进行有效的检测,具有较好的双重保护性能。     

子波变换在计算机网络信息智能处理中的应用

本文分析了子波变换自适应时频多尺度分辨的特点及相应的快速算法,子波变换与神经网络融合实现图像信息智能处理的技术。指出了用子波变换进行计算机网络图像信息降噪、数据压缩码和完美重构,可以提高计算机网络图像信息处理精度和实时传输速度。本文旨在促进信号处理和计算机网络学科交叉的研究。     

Denoising of multicomponent images using wavelet least-squares estimators

In this paper, we study denoising of multicomponent images. The presented procedures are spatial wavelet-based denoising techniques, based on Bayesian least-squares optimization procedures, using prior models for the wavelet coefficients that account for the correlations between the spectral bands. We analyze three mixture priors: Gaussian scale mixture models, Bernoulli-Gaussian mixture models and Laplacian mixture models. These three prior models are studied within the same framework of least-squares optimization. The presented procedures are compared to Gaussian prior model and single-band denoising procedures. We analyze the suppression of non-correlated as well as correlated white Gaussian noise on multispectral and hyperspectral remote sensing data and Rician distributed noise on multiple images of within-modality magnetic resonance data. It is shown that a superior denoising performance is obtained when (a) the interband covariances are fully accounted for and (b) prior models are used that better approximate the marginal distributions of the wavelet coefficients.     

Analysis of Barkhausen noise using wavelet-based fractal signal processing for fatigue crack detection

The paper presents new approach to Barkhausen noise signal processing for detection of fatigue crack. Barkhausen noise signal from mild steel samples under axial fatigue is investigated using fractal signal processing, particularly wavelet variance method. Based on repeatability analysis new algorithm is developed and applied to acquired signals. The influence of fatigue on fractal characteristics of Barkhausen noise is analyzed. Signal analysis reveals significant and repeatable changes in wavelet variance, spectral parameter and estimated Hurst exponent just after crack initiation. The results demonstrate high potential of fractal analysis of Surface Barkhausen noise applied to fatigue crack initiation detection.