一种基于Kullback-Leiblar距离的自动多门限图象分割方法及应用

本文根据Kullback－Leiblar距离的统计意义,提出一种基于Kullback-Leiblar距离的自动多门限图象分割方法,这种方法与NR．Pal和S．R．Pal等人提出的方法完全不同;此外此文还将从前研究的对象从单目标与背景推广到多目标及背景的自动多门限选取问题,最后用几幅困像进行算法检验;实践表明,该方法和其它方法相比较,效果较好．     

一种新的图象分割方法

大多数图象处理系统都需将目标从背景中分离出来。本文介绍一种新的自适应门限图象分割方法。门限曲面通过梯度值较大的象素点即可能的边缘点灰度值内插得到。     

一种改进的阈值分割算法在复合材料图像处理中的应用

针对纤维增强复合材料微观结构图像的特点,提出了一种改进的基于模糊思想的阈值分割算法,该算法利用整体阈值、局部阈值和像点梯度信息之间的关系,构造了一个双输入单输出的模糊逻辑系统,合理计算出像点的最佳分割阈值.实验结果证明了算法的有效性.     

Implementation of GPR Signals De-Noising Based on DSP

An important issue of ground-penetrating radar （GPR） signals analysis is de-noising thai is the guarantee of acquiring good detecting effect. The paper illustrates a successful application of digital single processor （DSP） based on wavelet shrinkage algorithm. In order to realize real-time GPP, signals analysis, some key issues are discussed such as the realization of fast wavelet transformation, the selection of CPU chip and the optimization of data movement. Experimenial results show that the DSP based application not only basically meets the real-time requirement of GPP, signals analysis, but also assures the quality of the GPR signals analysis.     

Adaptive Diagnosis for Fault Tolerant Data Fusion Based on α-Rényi Divergence Strategy for Vehicle Localization

When applying a diagnostic technique to complex systems, whose dynamics, constraints, and environment evolve over time, being able to re-evaluate the residuals that are capable of detecting defaults and proposing the most appropriate ones can quickly prove to make sense. For this purpose, the concept of adaptive diagnosis is introduced. In this work, the contributions of information theory are investigated in order to propose a Fault-Tolerant multi-sensor data fusion framework. This work is part of studies proposing an architecture combining a stochastic filter for state estimation with a diagnostic layer with the aim of proposing a safe and accurate state estimation from potentially inconsistent or erroneous sensors measurements. From the design of the residuals, using α-Rényi Divergence (α-RD), to the optimization of the decision threshold, through the establishment of a function that is dedicated to the choice of α at each moment, we detail each step of the proposed automated decision-support framework. We also dwell on: (1) the consequences of the degree of freedom provided by this α parameter and on (2) the application-dictated policy to design the α tuning function playing on the overall performance of the system (detection rate, false alarms, and missed detection rates). Finally, we present a real application case on which this framework has been tested. The problem of multi-sensor localization, integrating sensors whose operating range is variable according to the environment crossed, is a case study to illustrate the contributions of such an approach and show the performance.     

Wavelet-based edge correlation incorporated iterative reconstruction for undersampled MRI

Undersampling k-space is an effective way to decrease acquisition time for MRI. However, aliasing artifacts introduced by undersampling may blur the edges of magnetic resonance images, which often contain important information for clinical diagnosis. Moreover, k-space data is often contaminated by the noise signals of unknown intensity. To better preserve the edge features while suppressing the aliasing artifacts and noises, we present a new wavelet-based algorithm for undersampled MRI reconstruction. The algorithm solves the image reconstruction as a standard optimization problem including a ?₂ data fidelity term and ?₁ sparsity regularization term. Rather than manually setting the regularization parameter for the ?₁ term, which is directly related to the threshold, an automatic estimated threshold adaptive to noise intensity is introduced in our proposed algorithm. In addition, a prior matrix based on edge correlation in wavelet domain is incorporated into the regularization term. Compared with nonlinear conjugate gradient descent algorithm, iterative shrinkage/thresholding algorithm, fast iterative soft-thresholding algorithm and the iterative thresholding algorithm using exponentially decreasing threshold, the proposed algorithm yields reconstructions with better edge recovery and noise suppression.     

一种改进的QRS波检测方法

对QRS波群的检测算法进行研究,针对基于经验模式分解的传统QRS波检测方法存在的一些问题加以改进,提出新的检测方法.该方法利用心电信号经过经验模式分解后,QRS波能量主要分布在第1个固有模态函数IMF1的特性,通过对IMF1进行阈值收缩去噪、线性高通滤波以及非线性低通滤波等预处理突出QRS波的特征信息,最后利用自适应阈值法检测QRS波.研究结果表明:利用心率失常库MIT-BIH中的记录对该检测方法进行检验,正确检出率超过99.8%;该方法简单有效,准确率高,适于实际应用.     

Restricted thresholding: recovering smoothness and preserving edges

Restricted non linear approximation is a generalization of the N‐term approximation in which a measure on the index set of the approximants controls the type, instead of the number, of elements in the approximation. Thresholding is a well‐known type of non linear approximation. We relate a generalized upper and lower Temlyakov property with the decreasing rate of the thresholding approximation. This relation is in the form of a characterization through some general discrete Lorentz spaces. Thus, not only we recover some results in the literature but find new ones. As an application of these results, we compress and reduce noise of some images with wavelets and shearlets and show, at least empirically, that the L²‐norm is not necessarily the best norm to measure the approximation error.     

单通道光学相关实现击中与否运算

给出了一种单通道光学相关实现击中与否运算的算法和光学装置，此法基于形态学中的腐蚀运算和光学相关的关系及阈值操作，在非相干光学相关器中，将前景结构核元素编码为１，背景结构核元素编码为ａ（ａ满足一定条件），只需用原图像与前景和背景结构进行相关，然后对相关结果取阈值的Ｋ（前景结构核元素的像素数）即可得到击中与否运算，击中与否运算可以光学并行一步实现。