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01 基于 单片机 控制 节制 乒乓球 机器人 设计 实现 说明书 仿单 论文 开题 报告 讲演 呈文 外文 翻译

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01基于单片机控制的乒乓球捡球机器人设计与实现【说明书论文开题报告外文翻译】,01,基于,单片机,控制,节制,乒乓球,机器人,设计,实现,说明书,仿单,论文,开题,报告,讲演,呈文,外文,翻译

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毕 业 设 计（论 文）任 务 书1本毕业设计（论文）课题应达到的目的：通过毕业设计，使学生受到电气工程师所必备的综合训练，在不同程度上提高各种设计及应用能力，具体包括以下几方面：1. 调查研究、中外文献检索与阅读的能力。2. 综合运用专业理论、知识分析解决实际问题的能力。3. 定性与定量相结合的独立研究与论证的能力。4. 实验方案的制定、仪器设备的选用、调试及实验数据的测试、采集与分析处理的能力。5. 设计、计算与绘图的能力，包括使用计算机的能力。6. 逻辑思维与形象思维相结合的文字及口头表达的能力。2本毕业设计（论文）课题任务的内容和要求（包括原始数据、技术要求、工作要求等）：1.本 课 题 要 求 利 用 所 学 的 专 业 知 识 ， 进 行 乒 乓 球 捡 球 机 器 人 系 统 设 计 ， 要 求 能够 实 现 机 器 人 视 觉 识 别 、 姿 态 控 制 等 功 能 要 求 控 制 。2.设计系统的硬件电路和软件程序，包括详细的硬件设备配置，系统连接，程序调试等详细步骤；3.最终完成一篇符合金陵科技学院毕业论文规范的系统技术文档，包括各类技术资料，电路图纸，程序等；4.系统要有实际的硬件展示，并能够通电运行；5.本子系统要与整个系统能够配合运行；6.能够完成各项任务，参加最后的毕业设计答辩。毕 业 设 计（论 文）任 务 书3对本毕业设计（论文）课题成果的要求包括图表、实物等硬件要求： 1.按期完成一篇符合金陵科技学院论文规范的毕业设计说明书（毕业论文） ，能详细说明设计步骤和思路；2.能有结构完整，合理可靠的技术方案；3.能有相应的电气部分硬件电路设计说明；4.有相应的图纸和技术参数说明。5.答辩时完成实际系统的运行和展示。4主要参考文献： 1 蔡磊涵. CMOS 图像传感器在监控市场主导地位提升J. 中国安防. 2015(10) 2 黄万国,刘中涛,王小东. 基于专利数据的 CMOS 图像传感器技术发展态势分析J. 中国科技信息. 2014(09) 3 杨燕,陆然,韩颖姝,颜庙青,马圆. CMOS 图像传感器专利分析针对申请人以及目标国的分析J. 电视技术. 2013(S2) 4 陆然,杨燕,王晓华,张思秘. CMOS 图像传感器的关键专利分析J. 电视技术.2013(S2) 5 陆然,杨燕,王晓华,李静. 浅析图像传感器领域专利检索方法J. 电视技术.2013(S2) 6 包括图像传感器的设备、半导体装置及其制造方法J. 传感器世界. 2013(11) 7 姚立斌. 低照度 CMOS 图像传感器技术J. 红外技术. 2013(03) 8 德开发出新型有机图像传感器J. 传感器世界. 2013(02) 9 刘昌举,吴治军,祝晓笑,熊平,吕玉冰. 基于 4T 结构的高灵敏度 CMOS 图像传感器设计J. 半导体光电. 2013(03) 10 谭逸恒,朱丹,李松,潘晓. CMOS 图像传感器发展趋势J. 科技风. 2013(13) 11 余跃庆 马 兰 崔忠炜 李渊. 并联机器人开槽薄壁柔顺关节设计与实验*J. 农业机械学报. 2014(05) 12 史恩秀 陈敏敏 李 俊 黄玉美. 移动机器人智能搜索路径规划方法研究J. 农业机械学报. 2014(06) 13 刘刚 司永胜 冯娟. 农林作物三维重建方法研究综述J. 农业机械学报. 2014(06) 14 张雪华 1,2,刘华平 2,孙富春 2,高蒙 1,贺超 2. 采用 Kinect 的移动机器人目标跟踪J. 智能系统学报. 2014(01) 15 王奎民 1,赵玉飞 2,侯恕萍 3,孙海涛 2. 一种改进人工势场的 UUV 动碍航物规避方法J. 智能系统学报. 2014(01) 16 李贞辉 1,2,王洪光 1,王越超 1,姜 勇 1,岳 湘 1,2. 一种输电线巡检机器人自主抓线控制方法J. 吉林大学学报(工学版). 毕 业 设 计（论 文）任 务 书5本毕业设计（论文）课题工作进度计划：2015.11.04-2015.11.282015.11.29-2015.12.162015.12.17-2016.01.102016.02.25-2016.03.092016.03.09-2016.04.282016.04.29-2016.05.092016.05.09-2016.05.132016.05.14-2016.05.21在毕业设计管理系统里选题与指导教师共同确定毕业设计课题查阅指导教师下发的任务书，准备开题报告提交开题报告、外文参考资料及译文、论文大纲进行毕业设计（论文） ，填写中期检查表，提交论文草稿等按照要求完成论文或设计说明书等材料,提交论文定稿教师评阅学生毕业设计；学生准备毕业设计答辩参加毕业设计答辩，整理各项毕业设计材料并归档所在专业审查意见：通过 负责人： 2016 年 1 月 14 日 毕 业 设 计（论文） 开 题 报 告 1结合毕业设计（论文）课题情况，根据所查阅的文献资料，每人撰写不少于1000 字左右的文献综述： 随着入门对机器人技术智能化本质认识的加深，机器人技术开始渗透到人类活动的各个领域。其中，服务机器人作为一个重要分支，在国外研究领域已经得到了普遍重视。服务机器人的应用很广泛，主要从事维护保养、修理、运输、清洗、保安、救援、监护等工作。本课题设计的乒乓球捡球机器人，正是应用于乒乓球体育运动的自主式移动的服务机器人。 一、机器人技术简介 随着机器人技术的飞速发展，工业机器人已广泛应用于各个领域的工业现场。机器人（Robot）是自动执行工作的机器装置。它既可以接受人类指挥，又可以运行预先编排的程序，也可以根据以人工智能技术制定的原则纲领行动。它的任务是协助或取代人类工作的工作，例如生产业、建筑业，或是危险的工作。机器人一般由执行机构、驱动装置、检测装置和控制系统和复杂机械等组成。 驱动装置：是驱使执行机构运动的机构，按照控制系统发出的指令信号，借助于动力元件使机器人进行动作。它输入的是电信号，输出的是线、角位移量。 检测装置：实时检测机器人的运动及工作情况，根据需要反馈给控制系统，与设定信息进行比较后，对执行机构进行调整，以保证机器人的动作符合预定的要求。 控制系统：一种是集中式控制，即机器人的全部控制由一台微型计算机完成。另一种是分散（级）式控制，即采用多台微机来分担机器人的控制，如当采用上、下两级微机共同完成机器人的控制时，主机常用于负责系统的管理、通讯、运动学和动力学计算，并向下级微机发送指令信息；作为下级从机，各关节分别对应一个 CPU，进行插补运算和伺服控制处理，实现给定的运动，并向主机反馈信息。根据作业任务要求的不同，机器人的控制方式又可分为点位控制、连续轨迹控制和力（力矩）控制。 二、机器人智能化技术发展 智能型机器人是最复杂的机器人，也是人类最渴望能够早日制造出来的机器朋友。然而要制造出一台智能机器人并不容易，仅仅是让机器模拟人类的行走动作，科学家们就要付出了数十甚至上百年的努力。伴随着电力技术、自动化技术、微机技术和传感器技术的发展，推动了机器人朝着智能化方向不断进步。17681774 年间，瑞士钟表匠德罗斯父子，设计制造了三个由凸轮控制和弹箕驱动的自动机器人，至今还作为国宝保存在瑞士纳切特尔市艺术和历史博物馆内。1893 年，加拿大人摩尔设计制造了以蒸汽为动力的能行走的机器偶人“安德罗丁” 。这些事例标志着人类对于制造机器人从梦想到现实这一漫长道路上前进了 一大步。1958 年，美国联合控制公司的研究人员研制出第一台机器人原型。1959 年，美国的 UNIMATION 公司推出了第一台工业机器人。随着工业自动化技术和传感技术的不断发展，工业机器人在上世纪 60 年代逐渐被应用于喷涂和焊接作业当中，开始向实用化的方向迈进。到了 70 年代，工业机器人已经实现了实用化，当时的日本根据自身实际情况，加大了鼓励中小企业使用机器人的力度，这使日本机器人的拥有量在很短的时间内就超过了美国，一跃成为世界上的机器人大国。90 年代是机器人的普及时代，各类不同功能、不同作用的机器人开始大量应用于电子、汽车、服务等领域，并且为了满足人们的个性化需求，工业机器人的生产也日益趋向于多品种、多批次、小批量。市场的巨大需求在很大程度上刺激了机器人的加工和生产，并为机器人制造行业带来了巨额的经济效益，使其能够将更多的资金投入到新技术的研发和现有技术的完善当中，为机器人行业的进一步发展打下了坚实的基础。 进入 21 世纪以来，智能机器人获得较为迅速的发展。在计算机技术、网络技术、MEMS 技术等新技术发展的推动下，机器人技术正从传统的工业制造领域向医疗服务、教育娱乐、勘探勘测、生物工程、救灾救援等领域迅速扩展，适应不同领域需求的机器人系统被深入研究和开发无论从国际或国内的角度来看，复苏和继续发展机器人产业的一条重要途径就是开发各种智能机器人，以求提高机器人的性能，扩大其功能和应用领域。 三、乒乓球捡球机器人系统设计 调查发现，在乒乓球练习室里面，训练和比赛中地面上会出现很多出界和无效的乒乓球，需求花费专门人员去捡球，不仅效率低下，而且工作量巨大。基于这样的情况，本课题试设计出可以自动捡球的乒乓球机器人。该捡球机器基于单片机控制器，不仅价格合适而且控制功能非常强，采用图像识别、红外检测等技术可以轻松实现捡球的自动化。 参考文献： 1 许东伟,刘建群,林淦.乒乓球捡球机器人的设计与实现J.机床与液压,2014,(03). 2 王鹏宇.基于摄像头的智能小车决策系统研究D.大连理工大学.2009. 3 雷鹏飞，沈华东，高坎贷等.红外传感器在智能车避障系统的应用J.电脑与信息技术,2010,(04). 4 杨黎,余胜.红外避障系统的设计J.湖南人文科技学院学报,2013,(04). 5 徐沅坤.冯春成.基于 AVR 单片机的智能车避障系统的实现J.信息与电脑(理论版),2011,(12). 6 何奇文,彭建盛,周东,首家辉,葛姣龙.基于红外反射式传感器智能车系统的设计J.高师理科学刊,2008,(03). 7 陈懂.智能小车运动控制系统的研究与实现D.东南大学.2005. 8 孙颖.基于路径规划的智能小车控制系统研究D.青岛大学.2007. 9 王灏.机器人智能控制方法研究D.华南理工大学.1999. 10 刘荣.自动机器人轨迹控制系统及相关算法研究D.电子科技大学.2008. 11 苏凤,徐强,杨国庆.基于多传感器的智能车系统设计J.传感器世界,2012,(08). 12 王烁.基于图像处理技术的智能车研究D.兰州理工大学.2013. 13 郭戈,罗志刚.多传感器数据融合方法的研究与进展J.机电一体化,2003,(05). 14 胡房武.基于图像采集的智能车系统设计D.大连海事大学.2011. 15 廖天发,曹建忠,陈永源.基于 AT89S52 单片机的简易智能小车的设计J.科技资讯,2006,(21). 毕 业 设 计（论文） 开 题 报 告 2本课题要研究或解决的问题和拟采用的研究手段（途径）： 1、研究设计内容 本课题研究的基于单片机的乒乓球捡球机器人，能完成的控制有：采用风扇产生的吸力来实现捡球；通过红外传感器检测周围环境，自动避障；利用 CMOS 图像传感器识别乒乓球的物理位置。 2、设计途径 硬件系统：CMOS 图像传感器电路设计，捡球动作电路设计，红外传感器电路设计，机器人载体小车电路设计。 软件系统：CMOS 图像采集程序，红外传感器环境参数检测程序，机器人载体小车驱动程序，乒乓球物理地点计算程序。 毕 业 设 计（论文） 开 题 报 告 指导教师意见：1对“文献综述”的评语：综述内容较为丰富，参考文献合理，概括了课题所包含的研究内容的相关背景、基础知识、发展现状等，同时还对本课题所研究的任务进行了一定的阐述，对本课题的研究有一定的指导意义。2对本课题的深度、广度及工作量的意见和对设计（论文）结果的预测：本课题研究的任务是基于单片机控制乒乓球捡球机器人系统进行设计，应该说技术相对成熟，深度中等，但是涉及到的知识面较广，如果学生能认真对待，通过实例调研，查阅专业资料，相信能够实现最终的设计任务和结果，并对自己的专业应用能力是一个非常大的提高。3.是否同意开题： 同意 不同意指导教师： 2016 年 03 月 07 日所在专业审查意见：同意 负责人： 2016 年 03 月 08 日 译文题目： Digital Image Processing and Edge Detection 数字图像处理与边缘检测 2016 年 3 月 4 日Digital Image Processing and Edge Detection1. Digital Image ProcessingInterest in digital image processing methods stems from two principal applicantion areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for au- tenuous machine perception.An image may be defined as a two-dimensional function, f(x, y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, peels, and pixels. Pixel is the term most widely used to denote the elements of a digital image.Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike humans, who are limited to the visual band of the electromagnetic (EM) spec- trump, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves.They can operate on images generated by sources that humans are not accustomed to associating with images. These include ultra- sound, electron microscopy, and computer-generated images. Thus, digital image processing encompasses a wide and varied field of applications.There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vi- son, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of computing the average intensity of an image (which yields a single number) would not be considered an image processing operation. On the other hand,there are fields such as computer vision whose ultimate goal is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence (AI) whose objective is to emulate human intelligence. The field of AI is in its earliest stages of infancy in terms of development, with progress having been much slower than originally anticipated. The area of image analysis (also called image understanding) is in be- teen image processing and computer vision.There are no clear-cut boundaries in the continuum from image processing at one end to computer vision at the other. However, one useful paradigm is to consider three types of computerized processes in this continuum: low-, mid-, and high-level processes. Low-level processes involve primitive opera- tons such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. A low-level process is characterized by the fact that both its inputs and outputs are images. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. A midlevel process is characterized by the fact that its inputs generally are images, but its outputs are attributes extracted from those images (e.g., edges, contours, and the identity of individual objects). Finally, higher-level processing involves “making sense” of an ensemble of recognized objects, as in image analysis, and, at the far end of the continuum, performing the cognitive functions normally associated with vision. Based on the preceding comments, we see that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects in an image. Thus, what we call in this book digital image processing encompasses processes whose inputs and outputs are images and, in addition,encompasses processes that extract attributes from images, up to and including the recognition of individual objects. As a simple illustration to clarify these concepts,consider the area of automated analysis of text. The processes of acquiring an image of the area containing the text, preprocessing that image, extracting (segmenting)the individual characters, describing the characters in a form suitable for computer processing, and recognizing those individual characters are in the scope of what we call digital image processing in this book. Making sense of the content of the page may be viewed as being in the domain of image analysis and even computer vision,depending on the level of complexity implied by the statement “making sense.” As will become evident shortly, digital image processing, as we have defined it, is used successfully in a broad range of areas of exceptional social and economic value.The areas of application of digital image processing are so varied that some form of organization is desirable in attempting to capture the breadth of this field. One of the simplest ways to develop a basic understanding of the extent of image processing applications is to categorize images according to their source (e.g., visual, X-ray, and so on). The principal energy source for images in use today is the electromagnetic energy spectrum. Other important sources of energy include acoustic, ultrasonic, and electronic (in the form of electron beams used in electron microscopy). Synthetic images, used for modeling and visualization, are generated by computer. In this section we discuss briefly how images are generated in these various categories and the areas in which they are applied.Images based on radiation from the EM spectrum are the most familiar, especially images in the X-ray and visual bands of the spectrum. Electromagnet- ice waves can be conceptualized as propagating sinusoidal waves of varying wavelengths, or they can be thought of as a stream of mass less particles, each traveling in a wavelike pattern and moving at the speed of light. Each mass less particle contains a certain amount (or bundle) of energy. Each bundle of energy is called a photon. If spectral bands are grouped according to energy per photon, we obtain the spectrum shown in fig. below, ranging from gamma rays (highest energy) at one end to radio waves (lowest energy) at the other. The bands are shown shaded to convey the fact that bands of the EM spectrum are not distinct but rather transition smoothly from one to the other.Image acquisition is the first process. Note that acquisition could be as simple as being given an image that is already in digital form. Generally, the image acquisition stage involves preprocessing, such as scaling.Image enhancement is among the simplest and most appealing areas of digital image processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or simply to highlight certain features of interest in an image. A familiar example of enhancement is when we increase the contrast of an imagebecause “it looks better.” It is important to keep in mind that enhancement is a very subjective area of image processing.Image restoration is an area that also deals with improving the appearance of an image. However, unlike enhancement, which is subjective, image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation.Enhancement, on the other hand, is based on human subjective preferences regarding what constitutes a “good” enhancement result.Color image processing is an area that has been gaining in importance because of the significant increase in the use of digital images over the Internet. It covers a number of fundamental concepts in color models and basic color processing in a digital domain. Color is used also in later chapters as the basis for extracting features of interest in an image.Wavelets are the foundation for representing images in various degrees of resolution. In particular, this material is used in this book for image data compression and for pyramidal representation, in which images are subdivided successively into smaller regions.Compression, as the name implies, deals with techniques for reducing the storage required saving an image, or the bandwidth required transmitting it. Although storage technology has improved significantly over the past decade, the same cannot be said for transmission capacity. This is true particularly in uses of the Internet, which are characterized by significant pictorial content. Image compression is familiar (perhaps inadvertently) to most users of computers in the form of image file extensions, such as the jpg file extension used in the JPEG (Joint Photographic Experts Group) image compression standard.Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape. The material in this chapter begins a transition from processes that output images to processes that output image attributes.Segmentation procedures partition an image into its constituent parts or objects.In general, autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged segmentation procedure brings the process a long way toward successful solution of imaging problems that require objects to be identified individually. On the other hand, weak or erratic segmentation algorithms almost always guarantee eventual failure. In general, the more accurate the segmentation, the more likely recognition is to succeed.Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the bound- ray of a region (i.e., the set of pixels separating one image region from another) or all the points in the region itself. In either case, converting the data to a form suitable for computer processing is necessary. The first decision that must be made is whether the data should be represented as a boundary or as a complete region. Boundary representation is appropriate when the focus is on external shape characteristics, such as corners and inflections. Regional representation is appropriate when the focus is on internal properties, such as texture or skeletal shape. In some applications, these representations complement each other. Choosing a representation is only part of thesolution for trans- forming raw data into a form suitable for subsequent computer processing. A method must also be specified for describing the data so that features of interest are highlighted. Description, also called feature selection, deals with extracting attributes that result in some quantitative information of interest or are basic for differentiating one class of objects from another.Recognition is the process that assigns a label (e.g., “vehicle”) to an object based on its descriptors. As detailed before, we conclude our coverage of digital image processing with the development of methods for recognition of individual objects.So far we have said nothing about the need for prior knowledge or about the interaction between the knowledge base and the processing modules in Fig2 above. Knowledge about a problem domain is coded into an image processing system in the form of a knowledge database. This knowledge may be as slim- plea as detailing regions of an image where the information of interest is known to be located, thus limiting the search that has to be conducted in seeking that information. The knowledge base also can be quite complex, such as an interrelated list of all major possible defects in a materials inspection problem or an image database containing high-resolution satellite images of a region in con- lection with change-detection applications. In addition to guiding the operation of each processing module, the knowledge base also controls the interaction between modules. This distinction is made in Fig2 above by the use of double-headed arrows between the processing modules and the knowledge base, as op- posed to single-headed arrows linking the processing modules.2. Edge detectionThe image edge is one of image most basic characteristics, often is carrying image majority of informations。 But the edge exists in the image irregular structure and in not the steady phenomenon, also namely exists in the signal point of discontinuity place, these spots have given the image outline position, these outlines are frequently we when the imagery processing needs the extremely important some representative condition, this needs us to examine and to withdraw its edge to an image。But the edge examination algorithm is in the imagery processing question one of classical technical difficult problems, its solution carries on the high level regarding us the characteristic description, the recognition and the understanding and so on has the significant influence; Also because the edge examination all has in many aspects the extremely important use value, therefore how the people are devoting continuously in study and solve the structure to leave have the good nature and the good effect edge examination operator question。In the usual situation, we may the signal in singular point and the point of discontinuity thought is in the image peripheral point, its nearby gradation change situation may reflect from its neighboring picture element gradation distribution gradient.Edge detection is a terminology in image processing and computer vision, particularly in the areas of feature detection and feature extraction, to refer to algorithms which aim at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities. Although point and line detection certainly are important in any discussion on segmentation, edge detection is by far the most common approach for detecting meaningful disco unties in gray level.The image majority main information all exists in the image edge, the main performance for the image partial characteristic discontinuity, is in the image the gradation change quite fierce place, also is the signal which we usually said has the strange change place。The strange signal the gradation change which moves towards along the edge is fierce, usually we divide the edge for the step shape and the roof shape two kind of types (as shown in Figure 1-1).In the step edge two side grey levels have the obvious change; But the roof shape edge is located the gradation increase and the reduced intersection point.May portray the peripheral point in mathematics using the gradation derivative the change, to the step edge, the roof shape edge asks its step, the second time derivative separately.To an edge, has the possibility simultaneously to have the step and the line edge characteristic. For example on a surface, changes from a plane to the normal direction different another plane can produce the step edge; If this surface has the edges and corners which the regular reflection characteristic also two planes form quite to be smooth, then works as when edges and corners smooth surface normal after mirror surface reflection angle, as a result of the regular reflection component, can produce the bright light strip on the edges and corners smooth surface, such edge looked like has likely superimposed a line edge in the step edge. Because edge possible and in scene object important characteristic correspondence, therefore it is the very important image characteristic。For instance, an object outline usually produces the step edge, because the object image intensity is different with the background image intensity.We knew that, the edge examination essence is uses some algorithm to withdraw in the image the object and the background junction demarcation line.We define the edge for the image in the gradation occur the rapid change region boundary.The image gradation change situation may use the image gradation distribution the gradient to reflect, therefore we may use the partial image differential technology to obtain the edge examination operator.The edge examination algorithm has the following four steps : Filter: The edge examination algorithm mainly is based on an image intensity step and the second time derivative, but the derivative computation is very sensitive to the noise, therefore must use the filter to improve and the noise related edge detector performance.Needs to point out that, the majority filter have also caused the edge intensity loss while noise reduction, therefore, strengthens the edge and between the noise reduction needs compromised.Enhancement: Strengthens the edge the foundation is determines the image each neighborhood intensity the change value.The enhancement algorithm may (or partial) the intensity value has the neighborhood the remarkable change spot to reveal suddenly.The edge strengthens is generally completes through the computation gradient peak-to-peak value.P1-1 Processing resultEx