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The Vision Association Landscape

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RONALD MUELLER, VISION MARKETS


 
  Meet the author
Ronald Mueller is an expert in the global machine vision industry, with a Ph.D. in computer vision and machine learning. Based on his technical know-how, his experience as a corporate executive, and his first-hand experience in machine vision sales in Europe, North America, and China, he founded the consulting firm Vision Markets in 2014. He, his team, and the Vision Markets Network of experts are dedicated to making businesses thrive in this globalized growth market of machine vision; email: r.mueller@markets.vision.

A variety of associations around the world are trying to gather machine vision companies as members these days. The landscape of associations is almost as scattered as the market itself. To not miss out, companies need to join several organizations to be represented in different world regions and target industries.

This article provides an overview of what machine vision-related associations do, and the benefits of joining them. It is also an appeal to those in the industry to proceed with confidence, knowing machine vision will have a big role in solving the global challenges of the 21st century, and to unite forces to strengthen these associations so machine vision gets the attention it deserves.

The accompanying table lists associations from all over the world that are addressing vision technologies directly, or as part of a larger scope in countries where there is no dedicated machine vision association. The list is not comprehensive, but it includes a broad range of associations. It is sorted by industry scope, with dedicated vision associations listed first, and includes number of members.

As the table indicates, some associations focus on regions, some focus on technologies or industries.

The Automated Imaging Association (AIA), the China Machine Vision Union (CMVU), the European Machine Vision Association (EMVA), and the Japan Industrial Imaging Association (JIIA) are the most recognized internationally and work together on market data and technology standards. Among them, the AIA has the most elaborate business model. It is backed by the Association for Advancing Automation (A3) — which is a coalition of AIA with the Robotic Industries Association (RIA) and the Motion Control and Motors Association (MCMA).

AIA runs well-attended annual business conferences and its own trade shows. It also produces market data research and runs an education program.

The following services are provided by one or several of the machine vision associations listed in the table:

Association goals and activities

• Networking and collaboration

Machine vision is an extremely versatile field, both with respect to the required base technologies and in its application areas. Base technologies comprise optics, analog and digital electronics hardware, embedded software, algorithmics, high-end data processing, machine learning, and IT. Many machine vision OEMs operate with a handful to a few dozen employees. Therefore, partnerships and knowledge exchange between developers, purchasers, marketers, and sales forces is of great value for the sustainable success of each organization.

Almost all of the listed associations organize annual meetings to bring decision-makers together. These meetings are one of the key sources of funding for the organizations and thus remain open to everyone. Participants of member companies usually obtain a discount on fees, yet if an association fails to provide significant benefits beyond membership, many companies’ decision-makers participate at the conference just to build their network and save on the fees.


 
Worldwide Machine Vision Associations. *Based on publicly available information as of July 2019.

• Development of standards

A group of five associations are working together on the development of industrywide technology standards. This group, called G3, comprises AIA (North America), CMVU (China), EMVA (Europe), JIIA (Japan), and the Mechanical Engineering Industry Association (VDMA, Germany). They are working on developing standards for various aspects of vision systems, including:

Camera control interface: GenICam
Camera data interfaces: Camera Link, GigE Vision, USB3 Vision, CoaXPress, and Camera Link HS
Software, communication: OPC UA Vision Companion Specification
Camera performance measurement: EMVA1288
Embedded vision interface: under development
Open lens communication: under development

During semiannual International Vision Standards Meetings (IVSMs), the standardization community meets for one week at the site of one of their member companies from around the world. Dedicated working groups for the various standards jointly define the advancements of new release versions for better interoperability and performance, while the group chairs coordinate relevant compliance aspects between the standards. At the traditional plugfests, company representatives test the theoretical standardized interoperability between devices and software — for example, cameras, cables, frame grabbers, and software libraries.

Thanks to these activities, the groundbreaking GenICam and GigE Vision standards paved the way to the accelerated adoption of vision technologies by various industries, for which they simplified system design and reduced costs on a large scale.

Companies attending these meetings benefit from learning early about the technical developments in standardization, which enables them to prepare their organization’s development roadmaps for new standard releases and ensure the compliance of their products.
From culling market data to working in unison to develop industrywide standards, groups throughout the world are raising the profile of machine vision. But can they unite for greater impact?

• Market analysis

Several associations — including AIA, EMVA, and CMVU — regularly conduct market analysis by polling their members for sales numbers. The AIA and EMVA use more or less the same 7 to 9 product categories and focus on the sales volume occurring in North America and Europe. Both organizations share their figures on a quarterly basis. The CMVU provides detailed annual reports organized by product category and target application.

• Innovation transfer from academia to industry

The goal of transferring innovations from academia to industry can be found on the websites of many associations, but the extent of activities supporting knowledge transfer from academia varies considerably. A primarily academic organization, the British Machine Vision Association (BMVA), hosts an annual conference at which the best research papers are presented to the industry community (after evaluation by a peer-review process). Other associations offer annual awards for the best research work.

Since such activities require a high level of expertise and effort, it is hard to find qualified and independent managers to help with them. If supported with government funding, such important activities would likely be prioritized on the associations’ agendas.

• Education

In competition with dedicated providers of seminars and trainings, associations have set up educational seminar and webinar programs, either on the basics of machine vision to reach a large audience, or on specific subjects to provide a member company with a forum by which to win prospects. This business model can be further extended to qualification certificates, such as AIA’s Certified Vision Professional.

• Marketing support

An association’s website can serve as a useful platform for the marketing and promotion of member offerings — just how useful depends on the design, usability, content, ranking (and thus the number of monthly users) of the website. Email newsletters are another common marketing channel for offerings that are primarily of interest to other member companies. The greatest marketing benefits for small companies likely come from joint booth pavilions at machine vision trade shows that target member markets.

In the scattered market of machine vision players, and with many different associations competing internationally for companies that sign on for memberships, few organizations have managed to establish a solid business model. The primary ways associations make income are through fees — charged for membership, conferences, trade shows, standard licenses, and advertisement.

Bound by common goals

The global machine vision market for components showed an estimated annual revenue of just $4.1 billion for 2018. By comparison, that is 0.2 per mil of the U.S. GDP (gross domestic product). But consider how machine vision is a key enabler for solving the challenges in the 21st century. Here are just some examples of such challenges:

• Agriculture

In a few decades, a global population of 10 billion people will need to be fed1. Smart farming — including optimized fertilization, automated harvesting, and the monitoring and eradication of pests — will only be possible with machine vision.

• Mobility

By 2050, 66% of the world’s population will be living in urban areas — that is, 6.5 billion people2. Smart mobility concepts are needed to ensure maximum efficiency of the use of available space for traffic, which includes visual monitoring of road users. Machine vision will also play a crucial role in autonomous driving.

• Manufacturing

Price pressures, quality expectations, and lack of human resources — along with the need to maximize the yield of raw materials that are getting scarcer — are driving the need for manufacturing automation. Machine vision, in combination with robotics, is set up to fill this role.

• Logistics

Over 100 billion parcels are expected to be shipped worldwide in 2020, with B2B online retail expected to reach double the value of the B2C online market. Smart warehousing and omnichannel logistics rely on machine vision for continuous tracking, automated handling, and transportation of goods.

• Security

Currently, an estimated 380 million security cameras have been professionally installed worldwide. Human operators alone cannot monitor such a large amount of visual information. Machine vision, using machine learning techniques, can automatically identify scenes of interest for human review, or detect specific events and act upon them autonomously.

• Green technologies

The extensive use of the Earth’s resources over the last several decades, combined with a growing world population, has led to environmental pollution and a shortage of raw materials. While machine vision can have little direct effect on CO2 reduction, recycling is a major application field for greener technologies. Over 400 million tons of plastics are produced every year, and most will end as waste sooner or later. The efficient recycling of waste materials — enabled by machine vision — can help lessen sea pollution, reduce CO2 emissions through waste incineration, and save crude oil usage.

Breakthroughs in such areas require further investments in the development of machine vision technologies. Thus, machine vision deserves and needs to receive strong support through public funding of research programs. Politicians need to be made aware of the capabilities of machine vision for both the development and maximum exploitation of the technology’s potential, as well as be informed about the ethical considerations of misuse, especially in the areas of surveillance and the military.

Associations that lead with solid funding and broad international backing by many member companies are in a good position to give the machine vision community a voice with such important lobbying activities. In the days of ongoing consolidation of the machine vision market, it may also be the right time for a consolidation of the machine vision association landscape — rather than maintaining isolated regional clusters and national consortiums. Ultimately, it is up to member companies to push established associations in the right direction: to work together more closely, to invite delegates from other regions into their advisory boards, and to mutually share knowledge, member benefits, organized events, and ultimately organizational structures.

References

1. United Nations Department of Economic and Social Affairs (2019). World Population Prospects 2019: Highlights, https://population.un.org/wpp/publications/files/wpp2019_highlights.pdf.

2. United Nations Department of Economic and Social Affairs (2014 revision). World Urbanization Prospects, www.un.org/en/development/desa/publications/2014-revision-world-urbanization-prospects.html.

Vision Spectra
Autumn 2019
GLOSSARY
machine vision
Interpretation of an image of an object or scene through the use of optical noncontact sensing mechanisms for the purpose of obtaining information and/or controlling machines or processes.
Field of Viewmachine visionmarket datavision standards

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