论文（设计）基于小波分析和矩不变量的车型识别04293

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1、专业好文档文章编号：1009-8119（2006）03-0036-02基于小波分析和矩不变量的车型识别车德欣，李小平（北京理工大学信息科学技术学院计算机科学工程系，北京 100081）摘要提出了一种利用小波分析和矩不变量技术对汽车车型进行自动识别的方法，包括汽车车型图像的分割、矩特征的提取和车型自动识别。实验证明文中的算法有着较高的识别精度和良好的分类效果。关键字 车型，小波，矩不变量Automatic Vehicle Recognition Based on Wavelet Analysis and Invariant MovementChe Dexin， Li Xiaoping（Depar

2、tment of Computer Science and Engineering， School of Information Science and Technology， Beijing Institute of Technology， Beijing 100081）Abstracts: This paper discuses application of wavelet analysis and Invariant Movements on Automatic Vehicle Recognition. The wavelet analysis was used to get multi

3、-scale edge images， and the invariant moments were used to extract the features of image. Then we can compare similarity through the Euclidean distance between two images normalized moment vectors. The experimental result has demonstrated good performance in recognition of various vehicle types. It

4、can work accurately and quickly.Keywords: Automatic Vehicle Recognition， Wavelet， Invariant Movements1 引言随着我国经济快速发展，道路交通日益繁忙和拥挤，交通事故也日益增多。为有效地对道路上的车辆进行监控和管理，准确迅速识别、归类，有必要在电子收费系统中引入自动车型识别技术。本文提出了一种利用小波多尺度边缘检测和矩不变量特征对汽车车型进行自动识别的方法，包括汽车车型图像的分割、特征提取以及基于矩特征向量的车型自动识别和分类。2小波多尺度边缘检测 我们用二维信号来表示一幅图像，小波1多尺度边缘检

5、测的任务就是从图像中提取出车辆的边缘信息，以便将来的矩特征的提取。信号的多尺度边缘提取首先是在各个尺度上平滑信号，再利用平滑后的信号的一阶或二阶微商值来检测信号突变点。一阶微商极值点对应于信号二阶微商的零值点和平滑后信号的拐点。 定义在尺度2J时图像的模和幅角分别为: 其中和分别为原图像的水平和垂直方向的边缘信息。2若图像二进小波变换的局部极值点是点，则在该点上，M2j在沿由A2j给定的梯度方向上为局部最大2。即在尺度为2j时，图像的灰度突变点对应于沿梯度方向M2j的局部极大值。这些取极大值点的位置就给出了图像的一个多尺度边缘（如图1和图2所示）。通过对不同种类车型的识别结果表明，这种基于小波

6、变换模局部极大值的多尺度边缘检测方法，能够较好地检测出所需车型的轮廓信息，是一种比较有效的边缘检测方法，为后续的车型特征值的提取和分类打下了良好的基础。图1汽车原始图图2小波边缘检测效果3 矩不变量由于车型种类繁多，如何寻找具有良好描述和分类性能的特征以及如何提取这些特征就成为解决车型识别问题的关键。矩特征3是一种全局不变量，而且它对噪声不太敏感；矩的另一个特性是不管目标是否封闭，都能较好地识别目标。1962年，Hu. M. K4将代数不变量理论用于上述尺度规范化矩，构造出如下7个著名的矩不变量:不变矩特征的定义和计算公式建立在对一个区域内部灰度值或者是边界灰度值分析基础上的，是一个全局量的描

7、述，描述了对象的整体特征。在本课题中，通过小波多尺度边缘检测提取了图像的边缘信息后，接下来就是通过判断模板图和待匹配的图像的相似性判定车型。在此我们采用了欧氏距离的相似性5度量方法。模式样本向量X与Y之间的欧氏距离定义为:其中，n为特征空间的维数。若两幅图像位于同一个类型区域里，则欧氏距离D(x，y)较小。上面介绍的图像的7个矩特征对平移、旋转和尺度是不变量。在进行目标识别过程中，可以利用基于区域和基于边界的图像的7个矩不变量组成这个图像的特征向量，计算两幅图像的特征向量的欧氏距离作为两幅图像的相似度。因此利用不变矩的目标识别算法可按以下步骤进行：小波边缘检测提取目标的边界图。对图像目标的区域

8、图和边界图进行中心矩的计算；然后对两者中心矩归一化， 通过特征归一化来消除这种每个特征元素都的不同的物理意义上的幅度差异。在归一化的基础上，计算出目标图像和待匹配图像中目标的特征向量，最后计算两个向量之间的欧氏距离D，即为目标图像和测试图像的归一化特征向量的欧氏距离，预先设定一个阈值L以确定两者的相似度，如果DL则测试图像中的目标是要寻找的目标，反之则不是。4车型识别过程在实际应用中我们将车型定义为三大类：客车、货车和轿车，基本涵盖目前的绝大多数常见车型，其识别的算法总结如下：利用数码相机或高速图像捕捉设备获取汽车的侧面图像，得到车辆外形图像信号；对车辆图像进行必要的预处理以消除干扰噪声；对车

9、型图像进行小波多尺度边缘检测以获取边缘信号；提取车辆矩特征向量并计算其特征向量的相似度；比较待匹配图其与模板图的之间的欧氏距离D，判别车型。在图像的预处理过程中本系统使用了灰度化和平滑处理过程，以便更好地对图像进行边缘提取工作。5结论在对273幅不同类型汽车车型图像（其中客车类96辆、轿车类87辆、货车类辆90）进行的分类测试后，得出的识别结果为：客车类的正确识别93辆，正确率约为97，轿车类正确识别80辆，正确率约为92，货车类正确识别81辆，正确率90。通过对以上的车型识别的分析，初步得出该系统具有较好的识别率，为实际应用铺平了道路。不过，为了确保识别精度，本文采用的小波多尺度边缘检测算法

10、以及归一化特征向量欧式空间的比较计算量都比较大，这对于达到系统的实时识别要求还有一定的距离。到现在为止，各种不变矩的简化计算仍然在研究中，这为本系统的日后改进也提供了宽阔的思路。参考文献：1 Ingrid Daubechies. Ten Lectures on Wavelets. Rutgers University and AT&T Bell Laboratories.2 姚玉荣，章毓晋.利用小波和矩进行基于形状的图像检索J.中国图像图形学报，2000，A(3). 3 甘俊英，张有为.基于不变矩特征和神经网络的人脸识别模型. 计算机工程与应用，2002，7.4 Hu M K. Visual

11、pattern recognition by moment invariant. IRE Trans Information Theory，1962，8:1791875 柳林霞，陈杰，窦丽华.不变矩理论及其在目标识别中的应用. 火力与指挥控制， 2003，28:1315Editors note: Judson Jones is a meteorologist, journalist and photographer. He has freelanced with CNN for four years, covering severe weather from tornadoes to typh

12、oons. Follow him on Twitter: jnjonesjr (CNN) - I will always wonder what it was like to huddle around a shortwave radio and through the crackling static from space hear the faint beeps of the worlds first satellite - Sputnik. I also missed watching Neil Armstrong step foot on the moon and the first

13、space shuttle take off for the stars. Those events were way before my time.As a kid, I was fascinated with what goes on in the sky, and when NASA pulled the plug on the shuttle program I was heartbroken. Yet the privatized space race has renewed my childhood dreams to reach for the stars.As a meteor

14、ologist, Ive still seen many important weather and space events, but right now, if you were sitting next to me, youd hear my foot tapping rapidly under my desk. Im anxious for the next one: a space capsule hanging from a crane in the New Mexico desert.Its like the set for a George Lucas movie floati

15、ng to the edge of space.You and I will have the chance to watch a man take a leap into an unimaginable free fall from the edge of space - live.The (lack of) air up there Watch man jump from 96,000 feet Tuesday, I sat at work glued to the live stream of the Red Bull Stratos Mission. I watched the bal

16、loons positioned at different altitudes in the sky to test the winds, knowing that if they would just line up in a vertical straight line we would be go for launch.I feel this mission was created for me because I am also a journalist and a photographer, but above all I live for taking a leap of fait

17、h - the feeling of pushing the envelope into uncharted territory.The guy who is going to do this, Felix Baumgartner, must have that same feeling, at a level I will never reach. However, it did not stop me from feeling his pain when a gust of swirling wind kicked up and twisted the partially filled b

18、alloon that would take him to the upper end of our atmosphere. As soon as the 40-acre balloon, with skin no thicker than a dry cleaning bag, scraped the ground I knew it was over.How claustrophobia almost grounded supersonic skydiverWith each twist, you could see the wrinkles of disappointment on th

19、e face of the current record holder and capcom (capsule communications), Col. Joe Kittinger. He hung his head low in mission control as he told Baumgartner the disappointing news: Mission aborted.The supersonic descent could happen as early as Sunday.The weather plays an important role in this missi

20、on. Starting at the ground, conditions have to be very calm - winds less than 2 mph, with no precipitation or humidity and limited cloud cover. The balloon, with capsule attached, will move through the lower level of the atmosphere (the troposphere) where our day-to-day weather lives. It will climb

21、higher than the tip of Mount Everest (5.5 miles/8.85 kilometers), drifting even higher than the cruising altitude of commercial airliners (5.6 miles/9.17 kilometers) and into the stratosphere. As he crosses the boundary layer (called the tropopause), he can expect a lot of turbulence.The balloon wil

22、l slowly drift to the edge of space at 120,000 feet (22.7 miles/36.53 kilometers). Here, Fearless Felix will unclip. He will roll back the door.Then, I would assume, he will slowly step out onto something resembling an Olympic diving platform.Below, the Earth becomes the concrete bottom of a swimmin

23、g pool that he wants to land on, but not too hard. Still, hell be traveling fast, so despite the distance, it will not be like diving into the deep end of a pool. It will be like he is diving into the shallow end.Skydiver preps for the big jumpWhen he jumps, he is expected to reach the speed of soun

24、d - 690 mph (1,110 kph) - in less than 40 seconds. Like hitting the top of the water, he will begin to slow as he approaches the more dense air closer to Earth. But this will not be enough to stop him completely.If he goes too fast or spins out of control, he has a stabilization parachute that can b

25、e deployed to slow him down. His team hopes its not needed. Instead, he plans to deploy his 270-square-foot (25-square-meter) main chute at an altitude of around 5,000 feet (1,524 meters).In order to deploy this chute successfully, he will have to slow to 172 mph (277 kph). He will have a reserve pa

26、rachute that will open automatically if he loses consciousness at mach speeds.Even if everything goes as planned, it wont. Baumgartner still will free fall at a speed that would cause you and me to pass out, and no parachute is guaranteed to work higher than 25,000 feet (7,620 meters).It might not b

27、e the moon, but Kittinger free fell from 102,800 feet in 1960 - at the dawn of an infamous space race that captured the hearts of many. Baumgartner will attempt to break that record, a feat that boggles the mind. This is one of those monumental moments I will always remember, because there is no way Id miss this.6