没有合适的资源?快使用搜索试试~ 我知道了~
首页Machine Vision Handbook (机器视觉手册)
Machine Vision Handbook (机器视觉手册)
需积分: 47 74 下载量 117 浏览量
更新于2023-05-23
2
收藏 69.11MB PDF 举报
Machine Vision Handbook。非常经典的机器视觉手册(宝典)。英文版,带书签,高清。
1 Machine Vision for Industrial
Applications
Bruce G. Batchelor
Cardiff University, Cardiff, Wales, UK
1.1 Natural and Artificial Vision .................................................... 3
1.2 Artificial Vision . ................................................................. 5
1.3 Machine Vision Is Not Computer Vision . . . .. . . . .. . ............................10
1.3.1 Non-industrial Applications . . . . . . . . .. . . . .. . . . .. . . . .. . . . . . . . . . . . . . . . . . . . . . . .. . . . .. 15
1.3.2 Does It Matter What We Call Our Subject? . . . . .. . . . . . . . . . . . . . .. . . . . . . . .. . . . .. . . 19
1.4 Four Case Studies . . . . .. . . . .. . ...................................................20
1.4.1 Doomed to Failure . . . .. . . . .. . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . .. . . . .. . . . .. . . . . . . . . .. 21
1.4.2 A Successful Design . . . .. . . . . . . . . .. . . . .. . . . . . . . .. . . . .. . . . . . . . . .. . . . .. . . . .. . . . .. . . . . 22
1.4.3 Mouldy Nuts .. . . . .. . . . . . . . . . . . . . . . . . . . . . . .. . . . .. . . . .. . . . .. . . . . . . . . . . . . . .. . . . . . . . .. 22
1.4.4 A System That Grew and Grew .. . . . .. . . . . . . . .. . . . .. . . . . . . . . .. . . . .. . . . .. . . . .. . . . . 23
1.5 Machine Vision Is Engineering, not Science .. . . . .. . ............................25
1.5.1 Systems Engineering . .. . . . .. . . . . . . . . . . . . . . . . . . . . . . .. . . . .. . . . .. . . . .. . . . . . . . .. . . . . .. 25
1.6 Design Tools . . . .. ................................................................27
1.6.1 Image Acquisition . .. . . . .. . . . . . . . .. . . . .. . . . . . . . . .. . . . . . . . . .. . . . .. . . . . . . . .. . . . .. . . . . 28
1.6.2 Algorithm Design . . . . . . . . . . . . . .. . . . . . . . . .. . . . .. . . . . . . . . .. . . . .. . . . . . . . .. . . . .. . . . . . . 28
1.7 Overview of This Book .. . . . . . ...................................................31
1.7.1 General Principles .. . . . . . . . . . . . . . .. . . . . . . . .. . . . .. . . . .. . . . .. . . . .. . . . . . . . . .. . . . . . . . . . 32
1.7.1.1 Chapter 2: Inspecting Natural and Other Variable Objects . . . .. . . . .. . . . . . . . . . . . 32
1.7.1.2 Chapter 3: Human and Animal Vision . . . . . . . . .. . . . . . . . . . . . . .. . . . .. . . . . . . . . .. . . . 33
1.7.1.3 Chapter 4: Colour Vision .. . . . .. . . . . . . . . .. . . . . . . . . . . . . . .. . . . . . . . .. . . . .. . . . .. . . . .. . 34
1.7.1.4 Chapter 5: Light and Optics . . . . . . . . . .. . . . . . . . . .. . . . .. . . . .. . . . .. . . . .. . . . . . . . .. . . . . 34
1.7.1.5 Chapter 6: Telecentric, Fresnel and Micro Lenses . . . .. . . . . . . . . .. . . . . . . . . . . . . .. . . 35
1.7.1.6 Chapter 7: Illumination Sources .. . . . .. . . . . . . . . . . . . .. . . . . . . . . .. . . . .. . . . .. . . . .. . . . . 36
1.7.1.7 Chapters 8 and 40: Lighting-Viewing Methods . . . .. . . . .. . . . . . . . . . . . . . .. . . . . . . . . . 37
1.7.1.8 Chapter 9: Laser Scanners . . . . . . . . .. . . . . . . . . . . . . .. . . . .. . . . . . . . . .. . . . . . . . . . . . . . .. . . 38
1.7.1.9 Chapter 10: Cameras .. . . . .. . . . .. . . . . . . . . .. . . . . . . . . . . . . . .. . . . . . . . .. . . . .. . . . .. . . . .. . 39
1.7.1.10 Chapter 11: Selecting Cameras for Machine Vision . . .. . . . . . . . . . . . . .. . . . .. . . . .. . 40
1.7.1.11 Chapter 12: X-ray Inspection . . . . .. . . . .. . . . . . . . . .. . . . .. . . . . . . . .. . . . .. . . . . . . . . .. . . . 41
1.7.1.12 Chapter 13: Illumination-Invariant Image Processing . . . . . .. . . . . . . . . .. . . . .. . . . .. 42
1.7.1.13 Chapter 14: Basic Machine Vision Techniques . . . .. . . . .. . . . .. . . . .. . . . . . . . .. . . . . .. 42
Bruce G. Batchelor (ed.), Machine Vision Handbook, DOI 10.1007/978-1-84996-169-1_1,
#
Springer-Verlag London Limited 2012
1.7.1.14 Chapter 15: Imaging and Range Image Processing . . .. . . . . . . . . .. . . . .. . . . . . . . . . . . 43
1.7.1.15 Chapter 16: Colour Recognition . .. . . . . . . . .. . . . .. . . . .. . . . .. . . . .. . . . . . . . . .. . . . . . . . 44
1.7.1.16 Chapter 17: Algorithms, Approximations and Heuristics . . . . . . . .. . . . .. . . . .. . . . . 45
1.7.1.17 Chapter 18: Object Location Using the Hough Transform .. . . . . .. . . . .. . . . . . . . .. 46
1.7.1.18 Chapter 19: Morphological Image Processing . .. . . . . . . . .. . . . .. . . . . . . . . .. . . . . . . . . 46
1.7.1.19 Chapter 20: Image Processing Using Finite-State Machines . . . . . . . . . .. . . . . . . . . .. 47
1.7.1.20 Chapters 21 and 41: QT – Prototyping Image Processing System . . . . . . . . . . . . .. 48
1.7.1.21 Chapter 22: NeatVision: Development Environment for Machine Vision
Engineers . . . .. . . . . . . . .. . . . .. . . . .. . . . .. . . . .. . . . . . . . . .. . . . . . . . . . . . . .. . . . .. . . . .. . . . .. . 48
1.7.1.22 Chapter 23: Intelligent Image Processing Using Prolog .. . . . . . . . .. . . . .. . . . .. . . . . 49
1.7.1.23 Chapter 24: Pattern Recognition . . . . . . .. . . . . . . . . . . . . . .. . . . . . . . . .. . . . .. . . . . . . . .. . . 50
1.7.1.24 Chapter 25: Implementing Machine Vision Systems Using FPGAs . . . .. . . . .. . . . 51
1.7.2 Applications Case Studies and Practical Considerations . . . .. . . . .. . . . .. . . . . . . . . . 51
1.7.2.1 Chapter 26: Very Low-Cost In-Process Gauging System . . .. . . . .. . . . . . . . . . . . . . .. 52
1.7.2.2 Chapter 27: Automated Handling of Coils with Flying Leads . . .. . . . . . . . .. . . . .. 52
1.7.2.3 Chapter 28: A Telecentric Vision System for Broach Verification . . . . .. . . . .. . . . . 53
1.7.2.4 Chapter 29: Challenges of Low Angle Metal Surface (Crosshead)
Inspection .. . . . .. . . . .. . . . .. . . . . . . . . .. . . . . . . . . . . . . .. . . . .. . . . .. . . . .. . . . . . . . . .. . . . .. . . 53
1.7.2.5 Chapter 30: A Machine Vision System for Quality Grading of
Painted Slates . . . . . . .. . . . . . . . . . . . . .. . . . .. . . . .. . . . .. . . . . . . . . .. . . . .. . . . . . . . .. . . . .. . . . . 54
1.7.2.6 Chapters 31–35: Inspecting Glassware . . . . .. . . . .. . . . .. . . . . . . . .. . . . . . . . . . . . . . .. . . . 54
1.7.2.7 Chapter 36: Inspecting Food Products . . .. . . . .. . . . .. . . . . . . . .. . . . . .. . . . . . . . .. . . . .. 55
1.7.2.8 Chapter 37: Automatic Produce Grading System . . . . .. . . . . . . . . .. . . . .. . . . . . . . .. . . 56
1.7.2.9 Chapter 38: Analysing the Creasing Properties of Fabric . . . . . . . . .. . . . .. . . . . . . . .. 57
1.7.2.10 Chapter 39: Environmental, Social and Ethical Issues . . . . . . . . .. . . . .. . . . .. . . . .. . . 57
2 1 Machine Vision for Industrial Applications
Abstract: Machine Vision is related to, but distinct from Computer Vision, Image Processing,
Artificial Intelligence & Pattern Recognition. The subject is concerned with the engineering of
integrated mechanical-optical-electronic-software systems for examining natural objects and
materials, human artifacts and manufacturing processes, in order to detect defects and improve
quality, operating efficiency and the safety of both products and processes. It is also used to
control machines used in manufacturing. Machine Vision necessarily involves the harmonious
integration of mechanical handling, lighting, optics, video cameras, image sensors (visible, UV,
IR and X-ray sensor arrays, as well as laser scanners), digital, analogue and video electronics,
signal processing, image processing, computer systems architecture, software, industrial
engineering, human-computer interfacing, control systems, manufacturing, existing work
practices and quality assurance methods. Machine Vision is not a scientific endeavour; it is a
branch of Systems Engineering. Hence, consideration of application requirements pays a key
role in the design of practical vision systems. The basic philosophy of Machine Vision is set out
in this chapter and the structure of this book is outlined. This is a pragmatic, empirical,
experimental subject and is not unduly burdened by the search for mathematical rigour, or
theoretical purity. There is one simple design maxim: if it works, use it! This is justified here by
consideration of lessons learned from four practical application studies. Later in the book these
same ideas will emerge many times over.
1.1 Natural and Artificial Vision
Vision is critical for the survival of almost all species of the higher animals, including fish,
amphibians, reptiles, birds and mammals. In addition, many lower animal phyla, including
insects, arachnids, crustacea and molluscs possess well-developed optical sensors for locating
food, shelter, a mate, or a potential predator. Even some unicellular organisms are photosen-
sitive, although they do not have organs that can form high resolution images. Vision bestows
great advantages on an organism. Looking for food during foraging, or prior to giving chase, is
very efficient in terms of the energy expended. Animals that look into a crevice in a rock, to
check that it is safe to go in, are more likely to survive than those that do not do so. Animals that
use vision to identify a potential breeding partner are more likely to find a fit, healthy mate than
those that ignore its appearance. Animals that can see a predator approaching are more likely to
be able to escape capture than those that cannot. Compared to other sensing methods, based on
smell, sound and vibration, vision offers greater range, versatility, sensitivity and resolution.
Of course, vision is just as important to Homo sapiens as it is to any other animal species; life
in both the forest and a modern industrial society would be impossible without the ability to
see. Human society is organised around the highly refined techniques that people have
developed to communicate visually. People dress in a way that signals their mood, social
standing and sexual availability. Commerce is dependent upon hand-written, printed, and
electronic documents that all convey messages visually. Education relies heavily upon the
students’ ability to absorb information that is presented visually. Writers of technical books,
such as this, exploit the reader’s ability to understand complex abstract ideas through the visual
medium provided in printed text, diagrams and pictures. The leisure and entertainment
industries rely on the fact that vision is the dominant human sense. So important is vision
for our everyday lives that its loss is regarded by most people as one of the worst fates that could
happen to a human being.
Machine Vision for Industrial Applications 1 3
Manufacturing industry is critically dependent upon human beings’ ability to analyse
complex visual scenes. This is exploited in innumerable and highly variable tasks, ranging
from the initial design process, through initial forming, machin ing , finishing, assembly,
and quality control, to final packing. The value of vision as a safe way to sense many of the
dangers that exist in a factory does not need explanation. Vision is hygienic and does not
pollute the object under observation. With very few exceptions, the light levels needed to
inspect an industrial artifact do not affect it in any way. Vision is potentially very fast;
propagation delays from the object under inspection to sensor are typically just a few
nanoseconds. (The sensor and its associated processing system will normally introduce
much longer delays than this.) Vision provides a great deal o f very detaile d information
about a wide variety of features of an object, including its shape, colour, texture, surface
finish, coating, surface contamination, etc. Vision is extremely versatile and we can employ
a wide range of optical techniques to widen its range of application even further. For
example, it is possible to use stroboscopic light sources, structured ligh ting, colour filters,
coherent illumination polarise d light, fluorescence and many other clever optical ‘‘tricks’’
to sense a very broad range of physical prop erties.
Human vision is truly remarkable in its ability to cope with both subtlety and huge
variations in intensity, colour, form and texture. The dynamic range is enormous: about
10
14
:1. People can see extremely subtle changes of colour that are difficult to detect in
a machine. (This is why it is so difficult to match colours after automotive body repair.) People
can identity faces they know in a milling crowd, despite great changes in appearance that have
taken place since the individuals concerned last met. On the other hand, they can register two
matching pictures w ith great precision. A person can drive a car at high speed, yet a watch-
maker can make very fine adjustments to a mechanism. A player can strike the cue ball in the
game of billiards, or pool, with exquisite accuracy. A painter can capture the subtlety of colour
in a woodland scene on his canvas, while an experienced foundr y worker can identify the
temperature of an incandescent ingot from its colour very accurately. All of these visual skills
are exercised for inspection, grading, sorting, counting and a great many other tasks required
for manufacturing industry.
Despite its obvious benefit to industry, human vision has its limitations (
>
Fig. 1.1). Many
factors contribute to this, most notably fatigue, discomfort, illness, distraction, boredom, and
alcohol/drug use. People cannot work at the speed required to provide 100% inspection of the
products of many fast manufacturing processes. Boring repetitive tasks, such as examinig high-
speed conveyors carrying unstructured heaps of raw materials, are particularly prone to
unreliable performance. Many inspection tasks require precise dimensional/volumetric mea-
surement, which people cannot perform accurately. For safety’s sake, people working in
factories must not be exposed to high temperatures, toxic/stifling atmospheres, high levels of
smoke, dust and fumes, biological hazards, risk of explosion, excessively high noise levels, and
ionizing radiation. On the other hand, people can damage delicate products, by clumsy
handling and readily infect biological materials, by coughing, sneezing, shedding skin and
hair. Furthermore, people cannot always apply objective inspection criteria rigorously, partic-
ularly when aesthetic properties of highly variable items are to be judged. For these reasons,
engineers have long dreamed of building a machine that can ‘‘see.’’ Such a machine should be
able to sense its environment optically, using a video camera, or other imaging sensor,
interfaced to an electronic information-processing system: a standard computer, or dedicated
electronic hardware.
4 1 Machine Vision for Industrial Applications
1.2 Artificial Vision
For the moment, we will use the phrase Artificial Vision, since we do not yet want to get into
a detailed discussion about the precise meanings of the more commonly used terms Computer
Vision and Machine Vision. The application of Artificial Vision systems to manufacturing
industry motivates the techniques described in this handbook. This is an area where machines
are beginning to find application, in preference to people. Artificial Vision does not necessarily
attempt to emulate human, or animal, vision systems. Indeed, the requirement that industrial
vision systems must be fast, cost effective and reliable, together with our rather limited
knowledge about how people and animals actually see, make this approach unprofitable. By
studying vision in nature, we may gain insight but we must be flexible and pragmatic when
designing industrial vision systems.
When the author of this chapter is asked by a non-technical person what he does for
a living, he replies that, as an academic, he studies machines that consist of a television (more
properly, video) camera connected to a computer and uses them to inspect objects as they are being
made in a factory. Such a definition is acceptable around the dinner-table but not for a technical
book, such as this (
>
Fig. 1.2). In fact, designing a vision system that is of practical value in
manufacturing applications requires a multi-disciplinary approach, encompassing aspects of
all of the following technologies:
● Sample preparation
● Sample presentation to the camera
● Illumination
. Fig. 1.1
Human beings are easily distracted from boring tasks. Almost anything, for example attractive
members of the opposite sex, are more exciting to look at than objects moving past on an
industrial conveyor belt! (This diagram simply reflects the reality of life and is not intended to
denigrate either sex)
Machine Vision for Industrial Applications 1 5
剩余2207页未读,继续阅读
2012-10-04 上传
2019-07-26 上传
2019-06-16 上传
2019-06-16 上传
2018-08-08 上传
非我非我
- 粉丝: 5
- 资源: 6
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
最新资源
- Haskell编写的C-Minus编译器针对TM架构实现
- 水电模拟工具HydroElectric开发使用Matlab
- Vue与antd结合的后台管理系统分模块打包技术解析
- 微信小游戏开发新框架:SFramework_LayaAir
- AFO算法与GA/PSO在多式联运路径优化中的应用研究
- MapleLeaflet:Ruby中构建Leaflet.js地图的简易工具
- FontForge安装包下载指南
- 个人博客系统开发:设计、安全与管理功能解析
- SmartWiki-AmazeUI风格:自定义Markdown Wiki系统
- USB虚拟串口驱动助力刻字机高效运行
- 加拿大早期种子投资通用条款清单详解
- SSM与Layui结合的汽车租赁系统
- 探索混沌与精英引导结合的鲸鱼优化算法
- Scala教程详解:代码实例与实践操作指南
- Rails 4.0+ 资产管道集成 Handlebars.js 实例解析
- Python实现Spark计算矩阵向量的余弦相似度
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功