面向直方图加权的快速FCM的图像分割算法

提出一种改进的图像分割算法SWFFCM(special weight fast fuzzy C-means algorithm),该方法将需要聚类的二维像素点投影到一维灰度直方图空间,利用不同灰度值图像空间像素离散程度的区别,对一维灰度直方图进行线性加权,每一个灰度值变量使用四分位相对离差系数作为其权值.实验表明,算法减少了FCM(fuzzy C-means algorithm)算法的迭代数量,提高了图像聚类分割速度,减少了像素点错分比例,从而提高了图像的分割精度.     

一种快速的眼底图像视盘定位算法

目的：探讨一种快速的视网膜图像视盘定位方法。方法：提取RBG彩色视网膜图像的绿色通道作为处理对象;以每个像素点为中心取窗进行灰度调整操作;计算每个窗内灰度最大值,并根据该值重新估算窗的中心值;将该值作为中心像素点的灰度值。对提取到的图像进行二值化与形态学处理得到视盘区域和轮廓。结果：对DRIVE数据库测试图像进行了实验,提取视盘的平均运算时间为2.948s,视盘区域覆盖准确性92.5%。结论：较同类视盘提取算法速度更快,准确率达到平均水平,但对于视盘区域亮度偏暗的视网膜图像效果一般。     

基于背景图像差分的运动人体检测

在背景图像差分的基础上,提出一种改进的实时背景更新算法.通过背景学习过程,利用若干帧连续图像中每点像素灰度值样本,估算对应像素点的灰度值均方差.利用正态分布的"3σ原则"对图像背景进行实时更新,消除背景变化对人体分割的影响.提出基于图像平均灰度值的阈值分割算法,以提高算法对不同光照强度的适应能力.提出基于垂直投影图的阴影消除算法,分析表明,人体躯干和影子区的垂直投影特性不同,由此可以有效消除人体影子.实验结果表明,本算法在不同的光照情况下能够完整分割运动人体图像.     

一种新的声呐图像处理算法

针对高频声呐图像的目标检测问题,在分析声呐灰度图像像素点的灰度分布模型的基础上,提出了一种新的快速图像处理算法。该算法利用图像中各像素点处滑动窗内像素点的灰度分布模型的参数以及灰度分布模型和归一化灰度直方图的拟合误差为特征量,构造声呐图像的特征图,并采用自适应阈值算法进行图像处理。仿真结果表明该算法具有实时性好、准确度高的特点,并且可有效地克服背景干扰的影响,有利于人造目标的准确提取。     

基于优先权和匹配度量的图像修复算法

目的 针对当前图像修复算法主要通过固定单一模板大小实现修复块与匹配块之间的匹配度量来完成图像复原,导致其存在一定的模糊效应以及振铃效应等不足,这里提出基于改进优先权和匹配优化度量的图像修复算法。方法 首先,利用数据项构造平滑因子,建立优先权模型,度量待修复像素点的优先权,选定优先修复块。然后,制定四级模板大小,利用误差平方和函数,结合模板大小特性,构造匹配度量模型,用于实现修复块和匹配块之间的动态匹配,选取最优匹配模板,对待修复块进行填充修复。最后,利用待修复像素点及其邻域像素点的灰度值构造邻域灰度差分模型,用于对修复区域的边缘进行缝合,优化修复效果。利用最优匹配度量结果,构造置信度更新模型,对置信度项进行更新,实现图像修复。结果 仿真实验结果显示,与当前图像修复算法相比,所提算法具有更高的修复质量,其输出图像无模糊效应与振铃效应。结论 所提算法能够较好地对损坏图像进行复原,在图像信息处理领域具有一定的参考价值。     

基于图像边缘的灰度加权算法应用于LED管芯检测

LED管芯检测的像素级算法受到图像分割的影响,造成目标中部分像素的丢失.本文利用图像边缘区域的灰度加权算法,以求取物体边缘所在像素点的灰度来计算边缘位置.检测结果分析表明,该算法在检测精度上有了明显提高.     

一种基于FCM的图像分割方法

提出一种新的图像分割方法 FWFCM(fast walvet fuzzy C-means method),该方法对图像像素点的灰度进行模糊隶属度的分析,将需要聚类的像素空间投影到灰度直方图空间,从而减少了经典FCM算法的迭代计算量,提高了算法的收敛速度;并且利用小波变换的多分辨率的分析,抑制噪声点对图像分割的影响,提高了图像分割的精度.     

基于FSVM时域背景预测的红外弱小目标检测

本文提出了一种基于模糊支持向量机(FSVM)时域背景预测的红外弱小目标检测方法.首先针对前几帧图像中对应同一位置像素点的灰度值序列,利用模糊支持向量机进行函数拟合,并据此预测下一帧图像在该位置处像素点的灰度值:然后将原始图像与预测图像相减得到预测残差图像,利用基于二维Tsallis-Havrda-Charvat熵的阈值选取快速算法进行分割,并根据小目标运动的连续性和轨迹的一致性进一步分离噪声和小目标.文中给出了实验结果及分析,并与现有的检测红外小目标的空域和时域背景预测算法进行了比较.结果表明,本文提出的算法具有更高的检测概率,明显优于已有的基于背景预测的红外小目标检测算法.     

基于Canny算子的改进的图像边缘检测方法

本文简要介绍了Canny算子边缘检测的基本原理,并对其性能进行了分析和评价。针对传统Canny算子在滤波过程中存在的缺陷,文章提出了一种基于自适应平滑滤波的改进Canny边缘检测算子,该算法根据图像中像元灰度值的突变特性,自适应的改变滤波器的权值,在平滑图像的过程中使图像的边缘锐化。通过对实验图像的分析表明,改进的检测算法对图像边缘提取具有较好的检测精度和准确性。     

票据印刷号码定位识别技术研究

针对定额发票印刷特点,对图像进行相应的预处理,根据票面的特征,设计了票据号码定位算法,截取票据号码,提取号码像素的灰度信息作为特征对号码进行识别。实验证明,该方法有效且可行。     

基于云模型编码算法的图像消噪研究

为了提高图像消噪的质量,采用云模型编码算法.首先通过正态云发生器产生云滴;接着正云滴映射为图像中大于平均灰度的像素,负云滴映射为图像中小于平均灰度的像素,不同云滴的编码则表示不同的像素的灰度特征;然后根据编码规则将图像子块分为平滑块和非平滑块,平滑块区域保持其增强质量;最后给出了基于云模型编码算法的图像消噪模型和算法流程.实验仿真显示该文算法对图像消噪效果最好,能以较大概率找到全局最优解.     

DDoS Attack Detection via Multi-Scale Convolutional Neural Network

Distributed Denial-of-Service (DDoS) has caused great damage to the network in the big data environment. Existing methods are characterized by low computational efficiency, high false alarm rate and high false alarm rate. In this paper, we propose a DDoS attack detection method based on network flow grayscale matrix feature via multiscale convolutional neural network (CNN). According to the different characteristics of the attack flow and the normal flow in the IP protocol, the seven-tuple is defined to describe the network flow characteristics and converted into a grayscale feature by binary. Based on the network flow grayscale matrix feature (GMF), the convolution kernel of different spatial scales is used to improve the accuracy of feature segmentation, global features and local features of the network flow are extracted. A DDoS attack classifier based on multi-scale convolution neural network is constructed. Experiments show that compared with correlation methods, this method can improve the robustness of the classifier, reduce the false alarm rate and the missing alarm rate.     

一种似然曲线的图像拼接的改进算法

本文在大量调研国内外的相关算法的基础上,介绍了图像拼接的算法分类,并提出了一种基于灰度似然曲线的寻找匹配点的改进算法.其算法原理为首先进行匹配点的粗匹配,再进行改进后的似然曲线快速拼接,其不但保持了原始算法的精度,而且大大的加快了速度.通过仿真实验,在精度不变的情况下,对应于不同图像,速度较原始方法提高了约60%.     

Maskless projection lithography for the fast and flexible generation of grayscale protein patterns

Protein patterns of different shapes and densities are useful tools for studies of cell behavior and to create biomaterials that induce specific cellular responses. Up to now the dominant techniques for creating protein patterns are mostly based on serial writing processes or require templates such as photomasks or elastomer stamps. Only a few of these techniques permit the creation of grayscale patterns. Herein, the development of a lithography system using a digital mirror device which allows fast patterning of proteins by immobilizing fluorescently labeled molecules via photobleaching is reported. Grayscale patterns of biotin with pixel sizes in the range of 2.5 μm are generated within 10 s of exposure on an area of about 5 mm(2) . This maskless projection lithography method permits the rapid and inexpensive generation of protein patterns definable by any user-defined grayscale digital image on substrate areas in the mm(2) to cm(2) range.     

A CMOS focal-plane motion sensor with BJT-based retinal smoothing network and modified correlation-based algorithm

This work presents and implements a CMOS real-time focal-plane motion sensor intended to detect the global motion, using the bipolar junction transistor (BJT)-based retinal smoothing network and the modified correlation-based algorithm. In the proposed design, the BJT-based retinal photoreceptor and smoothing network are adopted to acquire images and enhance the contrast of an image while the modified correlation-based algorithm is used in signal processing to determine the velocity and direction of the incident image. The deviations of the calculated velocity and direction for different image patterns are greatly reduced by averaging the correlated output over 16 frame-sampling periods. The proposed motion sensor includes a 32/spl times/32 pixel array with a pixel size of 100/spl times/100 /spl mu/m/sup 2/. The fill factor is 11.6% and the total chip area is 4200/spl times/4000 /spl mu/m/sup 2/. The DC power consumption is 120 mW at 5 V in the dark. Experimental results have successfully confirmed that the proposed motion sensor can work with different incident images and detect a velocity between 1 pixel/s and 140,000 pixels/s via controlling the frame-sampling period. The minimum detectable displacement in a frame-sampling period is 5 /spl mu/m. Consequently, the proposed high-performance new motion sensor can be applied to many real-time motion detection systems.     

电镜图像的量子衍生增强算法

在量子信息处理框架下,从像素灰度归一化、像素量子态表示、量子测量、测量结果映射等方面,总结了现有量子衍生图像增强算法的处理流程。以 SARS-CoV-2 新冠病毒电镜图像增强为样本,结合实验分析,改进了量子衍生增强算子的加权方式,提出了灰度变换可调参数优化值的非迭代式确定算子。实验结果表明,量子衍生增强算法综合考虑了电镜图像的全局与局部信息,兼顾了对电镜图像对比度与亮度的调节,图像细节清晰、明暗适宜。     

基于可变形密集卷积神经网络的布匹瑕疵检测

针对传统布匹瑕疵检测方法无法适用于尺度变化大、面积占比小的瑕疵特征,提出一种基于可变形密集卷积神经网络模型。为了关注到图像中距离较远的特征信息,并避免捕获纹理信息,采用可变形卷积来增强特征的语义表达能力。通过在卷积层中设置卷积像素相对于中心像素各自的x,y方向偏移量,并利用反向传播训练偏移量以增加感受野的变形适应性。同时,采用密集连接的方式以保持模型不遗漏边缘瑕疵信息。最后,根据瑕疵类别预测和位置边框回归实现瑕疵的分类和定位检测。实验结果表明:该模型的平均检测精度和单类目标检测精度标准差分别为93.53%,2.5139,相比于其他方法更具有竞争力。     

岩石断面三维分形维数的灰度测算新方法

岩石断口表面形貌的定量描述是评价其力学行为的基础。灰度数字图像是指每个像素只有一个采样颜色的图像,根据采样位不同可以将灰度分为不同的级数,每个灰度对象都可以由一级或多级灰度表示。采用近距离高像素摄像机对岩石断面摄影,经灰度处理后,用像素—像素—灰度立方体盒子进行覆盖,测算出断面的三维分形维数。分析了摄影法的误差产生因素,并提出了相应的改进措施。     

基于均值聚类和循环导向滤波算法的农业图像去雾研究

采用均值聚类和循环导向滤波(MCLGF)算法对农业图像去雾进行了研究。首先,建立雾天图像光学模型;接着任意选取两个子特征向量作为初始聚类中心,根据分类结果,将有雾图像通过均值聚类分为天空区域和非天空区域,再通过天空区域中求取原图的最大像素值作为大气光值;然后,循环导向滤波实现细节平滑和边缘保持,最小化代价函数使导向滤波器的输出图像与输人图像差异最小;最后,给出了算法流程。实验仿真显示:该算法能够有效地降低图像中的雾气,结构相似度平均值为0.98、信息保真度平均值为0.96、图像信息熵可达9.12,可见边的正则梯度均值可达0.85,这4个评价指标相比其他算法均较好。     

Contrast enhancement of computed tomography images by adaptive histogram equalization‐application for improved ischemic stroke detection

The visualization of computed tomography brain images is basically done by performing the window setting, which stretches an image from the Digital Imaging and Communications in Medicine format into the standard grayscale format. However, the standard window setting does not provide a good contrast to highlight the hypodense area for the detection of ischemic stroke. While the conventional histogram equalization and other proposed enhanced schemes insufficiently enhance the image contrast, they also may introduce unwanted artifacts on the so‐called “enhanced image.” In this article, a new adaptive method is proposed to excellently improve the image contrast without causing any unwanted defects. The method first decomposed an image into equal‐sized nonoverlapped sub‐blocks. After that, the distribution of the extreme levels in the histogram for a sub‐block is eliminated. The eliminated distribution pixels are then equally redistributed to the other grey levels with threshold limitation. Finally, the grey level reallocation function is defined. The bilinear interpolation is used to estimate the best value for each pixel in the images to remove the potential blocking effect. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 153–160, 2012