机载高速视觉图像目标识别优化仿真研究

机载视觉采集器采集图像去噪的性能优劣,影响着目标识别的准确性.在机载高速视觉采集图像过程中,由于距离目标远,飞机速度快,图像采集的结果会包含大量的乘性噪声点,影响采集图像中目标像素可识别特征的质量.传统的图目标识别方法都是通过把这种乘性噪声模糊化,转换成加性噪声进行去噪处理然后加以识别,但是,在这种转换的过程中,会造成关键目标特征丢失,识别效果不好.提出基于改进多小波变换的机载高速视觉采集图像去噪识别方法.获取合适的基小波及小波分解层数目,对含有噪声的信号进行多小波变换操作,根据估计小波系数对信号进行重构操作,获取去噪后的机载高速视觉采集图像,优化识别过程.实验结果表明,利用改进算法进行机载高速视觉采集图像去噪,能够提高机载高速视觉采集图像的信噪比和图像质量.     

精密金属焊接坏点的视觉识别方法研究与仿真

研究高精密金属焊接坏点准确视觉识别的问题.由于高精密金属焊接坏点的颜色特征与金属本身特征相似,而在坏点很小并且与外部环境过于类似的干扰下,传统的基于像素差异的识别结果不精确,一旦焊接坏点分布的过于集中,干扰将更大,导致焊接坏点识别准确性降低.为解决上述问题,提出一种新的计算机视觉的高精密金属焊接坏点识别方法.采集高精密金属焊接坏点计算机视觉图像,针对金属图像进行特征提取,并对提取的特征进行离散小波变换处理,获取增强处理后的视觉图像.根据增强处理后的视觉图像特征值,获取高精密金属焊接坏点的分布密度,计算焊接坏点的空间位置,实现高精密金属焊接坏点识别.实验结果表明,利用改进算法进行高精密金属焊接坏点识别,能够提高识别的准确率,并且获取焊接坏点在高精密金属制品中的分布位置,为高精密金属制品的生产质量提供保障.     

基于视觉特征定位的物联网车祸现场识别

准确定位车祸现场有利于后期的营救工作.车祸发生过程中,对车辆碎片特征识别是判断车辆发生车祸的重要依据.但是车祸发生现场,无法避免由于现场环境信息紊乱造成的车辆碎片特征零散分布,与外部干扰特征形成混淆等弊端.传统的智能识别方法在大区域物联网车祸识别中,对车祸现场细小碎片的可识别特征难以准确分割,造成识别虚警率较高.为此,提出一种新的碎片视觉特征定位的物联网车祸现场识别方法.针对采集的车祸现场图像利用小波变换的方法进行碎片干扰特征滤波,消除由于环境信息紊乱造成的图像噪声,针对物联网环境下的多信号干扰问题,利用线性分类相关原理,对车祸现场图像中的细节特征进行分类提取,从而完成物联网框架下车祸现场的识别.实验结果表明,利用新算法进行物联网车祸现场识别,能够提高车祸识别精度.     

基于机器视觉高空飞机螺栓松动监测仿真

高空飞机受到自身振动、气流冲击和温度瞬变等干扰时,导致飞机螺栓结构松动、断裂或者脱落等问题.飞机螺栓结构具有复杂的动态性,传统的飞机螺丝辅助视觉松动检测方法在处理飞机螺栓复杂结构时,获取的螺丝帽和螺丝杆的相对位置过程中,受到采集环境的影响,很难得到稳定特征,导致图像监测误差,无法获取准确的螺栓松动监测结果.提出聚类RBF神经网络算法的高空飞机螺栓紧固方法.根采集检测部分螺丝视觉图像序列,并对上述图像进行离散变换处理,提取高空飞机螺栓图像的可识别分离特征.针对螺栓机器视觉图像特征误差较大的缺陷,构建特征分离目标函数,并建立聚类RBF神经网络模型,对特征进行多次校验计算,完成判断.实验结果表明,利用改进算法进行机器视觉的高空飞机螺栓松动监测,能够极大的提高监测的准确性.     

工作面刮板输送机煤流状态识别方法

煤矿井下工作面刮板输送机场景中存在的刮板输送机姿态多变、煤料形状不规则、设备安装位置受限、高粉尘、异物遮挡等不利因素,导致现有针对带式输送机场景的煤流状态识别方法无法有效在刮板输送机场景下进行工程化应用。针对上述问题,提出了一种基于时序视觉特征的工作面刮板输送机煤流状态识别方法。该方法首先利用DeepLabV3+语义分割模型获取工作面煤流视频图像中粗略煤流区域,并在此基础上通过线性拟合方法进行精细煤流区域定位与分割,实现煤流图像提取;然后将煤流图像按视频时序进行排列,构成煤流图像序列;最后采用C3D动作识别模型针对煤流图像序列进行特征建模,实现煤流状态自动识别。实验结果表明：该方法能准确获取煤流图像并自动、实时识别煤流状态,煤流状态平均识别准确率达92.73%;针对工程化部署应用,利用TensorRT对模型进行加速处理,对于分辨率为1 280×720的煤流视频图像,整体处理速度为42.7帧/s,满足工作面煤流状态智能监测实际需求。     

长视距空战中可疑目标的快速识别模型仿真

研究长视距空战中可疑目标的快速识别问题,由于在长视距空战中,可疑目标与空中识别设备之间存在较长的距离,采集目标比较弱小,特征模糊.目标一旦过于接近,会形成堆积盲区,传统的识别方法在遇到远距离可疑目标时,由于距离太远,会出现目标堆积盲区,导致对目标分割出现错误,无法准确识别目标.为解决上述问题,提出采用主成分聚类算法的长视距空战中可疑目标的快速识别方法.针对可疑目标采集大量的计算机视觉图像,提取计算机视觉图像中可疑目标的特征,将上述特征作为可疑目标识别的依据.针对所有的可疑目标识别特征,进行可疑目标的快速识别,并对识别到的可疑目标进行累加运算,实现可疑目标的智能识别.实验结果表明,利用改进算法进行长视距空战中可疑目标的快速识别,能够极大的提高识别的准确性.     

多干扰高温辐射体图像目标的分类识别方法

在基于CCD图像传感器的非接触高温温度场软测量中,为了计算高温图像目标的温度,必须先从辐射图像中准确识别待测目标。由于工业现场采集的高温熔体图像中存在多种噪声,导致图像目标难以准确识别。提出一种目标图像分类识别方法,即先利用多光谱图像分割方法来减少甚至消除各种高温噪声;然后运用改进的最大类间方差法进一步分割以去除烟雾干扰;最后运用数学形态学方法消除分割图像中的游离点和孔洞,使目标边缘光滑。实验结果表明,该方法能够从多种干扰图像中准确识别出高温辐射体图像目标,有较强的实用性。     

旋翼无人机变速下的多图像目标监测仿真

旋翼无人机在变速状态下的多图像目标识别对飞行性能有着重要的影响.旋翼无人机变速状态下,采集的多目标图像受到飞行不定速度的影响,造成采集的识别目标图像之间会出现可识别特征叠加、特征纯正缝隙和重叠等问题,造成可识别图像特征不连续.利用传统算法识别飞机变速下采集的多目标图像特征,无法对不联系的图像特征进行建模,一旦发生飞行速度变化,会造成无人机多目标识别结果出现较大偏差.提出采用经验模态分解算法的旋翼无人机多目标识别方法.根据旋翼无人机采集的不同目标的航拍图像,进行经验模态分解,针对分解结果进行重构,实现变速下多目标航拍图像融合.将融合处理后的结果,进行特征点提取和空间位置计算,实现旋翼无人机的多目标识别.实验结果表明,利用改进算法进行旋翼无人机的多目标识别,能够提高变速下多图像目标识别的准确性.     

基于嵌入式的汽车异常移动视觉监控仿真分析

研究汽车异常移动的准确视觉监控问题.汽车异常移动是指异于正常驾驶行为的特征,由于汽车运动过程的相似性会给汽车异常移动行为识别带来较大的干扰,传统的汽车异常移动识别方法中,在汽车晃动、轻微移动等行为干扰下,会产生误判.为解决上述问题,提出了一种利用嵌入式技术配合光流运动恢复算法的汽车异常移动视觉监控方法.设计一套嵌入式监控流程,针对传感器采集的汽车视觉监控图像,进行光流运动恢复,抗击由于汽车晃动或者轻微移动造成的行为干扰,为汽车异常移动视觉监控提供依据.对汽车监控图像的光流恢复结果进行背景分离,获取汽车异常移动监控结果.实验结果表明,利用改进算法进行嵌入式的汽车异常移动视觉监控,能够消除干扰,极大的提高监控的准确性,完成汽车异常移动的准确识别.     

基于彩色图像的机器人视觉声纳实现

着重分析了彩色图像中物体的识别方法 ,对如何利用彩色图像的颜色信息进行图像分割、目标识别进行了较为深入的讨论。提出了视觉声纳的概念 ,研究了从二维视觉信息中获取目标精确位置的算法。实验表明该算法可以得到相当精确的结果     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.     

Editorial: Special issue on recent advances in hybrid control theory and applications

正The demands of a rapidly advancing technology for faster and more accurate controllers have always had a strong influence on the progress of automatic control theory.In recent years control problems have been arising with increasing frequency in widely different areas,which cannot be addressed using conventional control techniques.The principal reason for this is the fact that a highly competitive economy is forcing systems to operate in regimes where     

Information for Authors

正Aim The Journals of Zhejiang University-SCIENCE(A/B/C)areedited by the international board of distinguished Chinese andforeign scientists,and are aimed to present the latest devel-opments and achievements in scientific research in China andoverseas to the world’s scientific circles,especially to stimulateand promote academic exchange between Chinese and for-eign scientists everywhere.     

Enhancing a leaf radiative transfer model to estimate concentrations and in vivo specific absorption coefficients of total carotenoids and chlorophylls a and b from single-needle reflectance and transmittance

The relative concentrations of different pigments within a leaf have significant physiological and spectral consequences. Photosynthesis, light use efficiency, mass and energy exchange, and stress response are dependent on relationships among an ensemble of pigments. This ensemble also determines the visible characteristics of a leaf, which can be measured remotely and used to quantify leaf biochemistry and structure. But current remote sensing approaches are limited in their ability to resolve individual pigments. This paper focuses on the incorporation of three pigments—chlorophyll a, chlorophyll b, and total carotenoids—into the LIBERTY leaf radiative transfer model to better understand relationships between leaf biochemical, biophysical, and spectral properties.Pinus ponderosa and Pinus jeffreyi needles were collected from three sites in the California Sierra Nevada. Hemispheric single-leaf visible reflectance and transmittance and concentrations of chlorophylls a and b and total carotenoids of fresh needles were measured. These data were input to the enhanced LIBERTY model to estimate optical and biochemical properties of pine needles. The enhanced model successfully estimated reflectance (RMSE = 0.0255, BIAS = 0.00477, RMS%E = 16.7%), had variable success estimating transmittance (RMSE = 0.0442, BIAS = 0.0294, RMS%E = 181%), and generated very good estimates of carotenoid concentrations (RMSE = 2.48 µg/cm², BIAS = 0.143 µg/cm², RMS%E = 20.4%), good estimates of chlorophyll a concentrations (RMSE = 10.7 µg/cm², BIAS = − 0.992 µg/cm², RMS%E = 21.1%), and fair estimates of chlorophyll b concentrations (RMSE = 7.49 µg/cm², BIAS = − 2.12 µg/cm², RMS%E = 43.7%). Overall root mean squared errors of reflectance, transmittance, and pigment concentration estimates were lower for the three-pigment model than for the single-pigment model. The algorithm to estimate three in vivo specific absorption coefficients is robust, although estimated values are distorted by inconsistencies in model biophysics. The capacity to invert the model from single-leaf reflectance and transmittance was added to the model so it could be coupled with vegetation canopy models to estimate canopy biochemistry from remotely sensed data.     

Mr. Twinn’s bombes

This article discusses the history and design of the special versions of the bombe key-finding machines used by Britain’s Government Code & Cypher School (GC&CS) during World War II to attack the Enigma traffic of the Abwehr (the German military intelligence service). These special bombes were based on the design of their more numerous counterparts used against the traffic of the German armed services, but differed from them in important ways that highlight the adaptability of the British bombe design, and the power and flexibility of the diagonal board. Also discussed are the changes in the Abwehr indicating system that drove the development of these machines, the ingenious ways in which they were used, and some related developments involving the bombes used by the U.S. Navy’s cryptanalytic unit (OP-20-G).