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2008年10月整理并翻译，欲参考或转载请询 。非常欢迎指正，希望共同学习。

机器视觉浅谈

什么是机器视觉，它来自何方，去向何往，什么是它的核心技术，什么是它的应用领域，这篇小文章可能会给你一些启示。（译者）

During the period 1970 - 1990, Japan taught Europe and America the importance of quality in manufactured goods. The West learned the hard way: markets were quickly lost to companies whose names were hitherto unknown. Many long established and well respected Western companies were unable to meet the challenge and consequently failed to survive. Those that did were often faced with difficult years, as their share of the market shrank. Most companies in Europe and America have largely come to terms with this and now realise that quality has a vital role in establishing and maintaining customer loyalty. In the present climate of opinion, any technology which improves or simply guarantees product quality is welcome.

上世纪70到90年代，日本教会了欧洲和美洲生产过程中质量的重要性。西方举步维艰：市场被一些至今未知其名的公司占据。许多之前表现良好的西方公司由于难以应对挑战而最终无法生存。它们通常面对了艰难年代：市场占有率的萎缩。欧洲和美洲的大部分公司为此付出了巨大代价，如今它们认识到了质量是建立并保持客户忠诚度的关键因素。大量建议被提出来，而能够改善甚至仅仅是保证产品质量的任何技术都大受欢迎。

Machine vision is a relatively new technology, which has much to offer manufacturing industry in improving product quality and safety, as well as enhancing process efficiency and operational safety. Machine vision owes its rising popularity to one major factor: optical sensing is inherently clean, safe (because it a non-contacting technology) and very versatile. It is possible to do certain things using vision (both human and machine) that no other known sensing method can achieve - imagine trying to sense stains, rust or surface corrosion by any other means.

机器视觉就是一个相关的新技术，它对生产工业中提高产品质量和安全，并增强生产过程效率和安全起到了很大的作用。机器视觉之所以能够快速普及的一个主要原因是：光学传感技术所固有的清洁、安全（由于它是非接触技术）和通用性。通过视觉（包括人眼和机器）可以实现没有任何已知的传感技术能够实现的诸多任务，包括污点、铁锈和表面腐蚀等。

Designing a machine vision system is like assembling a jigsaw.

设计一套机器视觉系统就像是在组合一幅拼图。

Among other component technologies machine vision involves the digitisation, manipulation and analysis of images, usually within a computer, a subject which is also covered by the terms image processing and computer vision. However, we must emphasise that machine vision, computer vision and image processing are not synonymous. None is a subset of either of the others. Computer vision is a branch of Computer Science, while machine vision is an area of specialisation within Systems Engineering. Notice, in particular, the use of the words "Science" and "Engineering" here. Machine vision does not necessarily imply the use of a computer; specialised image processing hardware is often used to obtain higher processing speeds than a conventional computer can achieve.

机器视觉的其他组件技术包括了图像数字化、图像操作和图像分析，通常是使用计算机来完成，这是一个同样被图像处理专业和计算机视觉技术所覆盖的范畴。但是我们必须强调，机器视觉，计算机视觉和图像处理并非等同的。没有任何一个概念是另一个概念的子集。计算机视觉是计算机科学的一个分支，而机器视觉是系统工程领域的一个具体化。特别注意，在此处“科学”和“工程”两个词的不同。机器视觉并非指必须使用计算机，其他专用的图像处理硬件也同样常用于获取比传统计算机所能达到的更高的处理速度。

Machine vision system for industry first received serious attention in the mid-1970s, although the proposal that a video system be used for industrial inspection was first made in the 1930s. Throughout the early 1980s, the subject developed slowly, with a steady contribution being made by the academic research community, but with only limited industrial interest being shown. It seemed in the mid-1980s that there would be a major boost to progress, with serious interest being shown in vision systems by the major American automobile manufacturers. Then, came a period of serious disillusionment in the USA, with a large number of small vision companies failing to survive. In the late 1980s and early 1990s, interest has grown markedly, due largely to significant progress being made in making fast, dedicated image digitisation and processing hardware. In the mid-1990s, the role of the general purpose processor is being revised, with the modern RISC processors offering high processing speed on a standard computing platform. Throughout this period, academic workers have been steadily proving feasibility in a very wide range of products, representing all of the major branches of manufacturing industry.  

尽管早在30年代视频系统方案就被用于工业检测，但直到70年代中期，用于工业的机器视觉系统才{dy}次得到重视。整个80年代早期，机器视觉都发展缓慢，主要是学术研究机构开展了一些工作，很少引起工业方面的兴趣。到了80年代中期，机器视觉有了一个巨大的推动力，这就是美国汽车生产商对视觉系统显示了浓厚的兴趣。之后，美国机器视觉领域迎来了一个严重的幻灭时代，许多小视觉公司倒闭。到了80年代末90年代初，大大得益于快速、专用的图像数字化和处理硬件的发展，机器视觉得到了广泛的关注。在90年代中期，随着现代RISC处理器提供了在标准计算平台的高处理速度，通用处理器的角色被颠覆了。在这个时期，学术工作者逐步证明了许多产品的可行性，取代了所有制造工业的主要分支。

Soon after starting work, machine vision is seen as a confusing jumble of disconnected ideas.

刚刚开始不久，机器视觉就好像变成了一个被错误思路引入歧途的拼图。

Industrial image processing systems, which necessarily form part of a vision system, have developed very considerably in the last decade. In addition, there have been major advances in other component technologies: image sensors, specialised lighting units, lenses and advisor (CAD) programs, which guide a vision engineer through the initial stages of the design process. However, systems integration remains the key factor for the successful design and operation of a machine vision system.

作为视觉系统必不可少的组成部分，工业图像处理系统在最近十年内得到了很大发展。另外，其他组件技术也有很大进步：图像传感器、专用光源单元、镜头和辅助设计软件（CAD），这些能够指导视觉工程师跳过设计过程的原始阶段。然而，系统集成在成功设计和操作一套机器视觉系统的过程中保留了其关键角色。

There are numerous techniques for manipulating images that are either not used, at all, or are used very infrequently in machine vision. Wherever there are problems of computational speed, machine vision systems engineers will either seek another solution, or avoid the problem entirely. Standard image processing techniques are able to achieve some remarkable results but they could not be described as being intelligent. By adding a certain level of intelligence, we are able to do certain things that would otherwise be impossible. For example, analysing the image of a standard ("analogue") clock, in order to tell the time is one such task that could not be solved using "traditional" image processing methods working on their own.

在机器视觉中，有很多操作图像的技术从来或者极少被用到。无论什么情况下都有计算速度的问题，机器视觉系统工程师必须寻求其他解决方法或xx避免问题出现。标准图像处理技术能够获取很好的结果，但是它们并不能被称为是智能的。提到智能水平，我们必须能够处理任何意外情况。例如分析标准（“模拟”）时钟图像，想识别时间就是一个不能够通过运用“传统”图像处理技术自身实现的任务。

There is a serious bottleneck in the design of machine vision systems: a high level of Eventually, the pieces fit together. However, if one piece is missing, the result is imperfect; system integration is incomplete. skilled man-power is needed to achieve an effective design. To illustrate the problem, consider the case of just one organisation, which has over 60000 products. That company operates a policy which tries to maintain at least 25% of its sales on products that are not more than 5 years old. Simple arithmetic shows that over 10 new product are being introduced by that one company alone, every working day. If we were to use a machine vision system on only 1% of those new product lines, we would need to design, build, install and test a new system once every 2 weeks. At the moment, the design process typically takes several months and there are simply not enough machine vision engineers to provide that kind of level of support, even for that one company. We desperately need more well-educated machine vision systems engineers. We also need improved design tools. By claiming that machine vision is a flexible technology, without having the man-power available to fulfil that boast is simply foolish. Such "overselling" of the virtues of machine vision technology was largely responsible for the collapse in credibility and confidence in the mid-1980s, to which we referred earlier. We need both improved educational material and better engineering tools, if we are to meet the challenge that this subject imposes upon us. Working in such a flexible and potentially beneficial technology carries responsibilities, because it is upon our shoulders that its future development and exploitation lies.  

在设计机器视觉系统中存在一个严重的瓶颈：高水平的最终系统，所有组件严丝合缝。如果任何一块出了问题，结果都是不xx的，系统集成都没有完成。需要熟练的工程师以实现高效的设计。举例来说，仅考虑单个组织的案例，生产了60000套视觉产品。该公司执行一项策略，试图对其生产的产品中的至少25%的不超过5年的产品进行维护。简单来算，每个工作日单个公司要生产10套新产品。即便假设我们仅在1%的新生产线上使用机器视觉系统，我们每2周要设计，制造，安装和测试一套新系统。那时候，设计过程通常需要花费几个月时间，而且即使对一家公司而言也没有足够的机器视觉工程师来提供优质的支持。我们迫切需要更多的接受了良好教育的机器视觉工程师。我们还需要更好的设计工具。由于机器视觉是一种灵活的技术，没有足够的人力资源而夸下海口简直就是蠢货。这种“销售过量”成了机器视觉技术是病毒，这就是我们之前提到的80年代中期机器视觉技术可信性和可靠性崩溃的{zd0}原因。如果我们想面对出现在面前的挑战，我们就需要改善的教材和更好的工程工具。在这一灵活而充满潜在利益的技术中工作担负着责任，因为在我们肩膀上的是它的未来发展和成长。

System integration is complete; all elements are now in place. The result is perfect (machine) vision.

系统集成完成了，所有的组件都各归其位。结果就是xx的（机器）视觉。