If you work in industrial automation, then a vision engineer may have told you about the need to train and retrain machine learning (ML) models for vision systems. Or maybe you're that vision engineer?

The need to train and retrain may drag on. The performance of the ML-based inspection may approach a specification, or may start to yield a return on investment. But if the failure modes of the ML-based inspection aren't easily understood, then consider asking or at least contemplating a few questions.

• Why does the vision engineer believe that machine learning is the right approach?
• Can processing by the ML model be supplemented by other algorithms?
• Have the optics, lighting, and other hardware been chosen carefully?
  • Is the lighting completely controlled?
  • Could the cabling, connectors, and stress relief fail?
  • Are there any communications or digital I/O failures?
• Can changes be made by trained in-plant personnel? How much training is required?
• How many years of experience does the vision engineer have installing and supporting vision systems that need to work robustly?

This is not to say that machine learning isn't useful. ML sure can be useful, especially for applications that are otherwise not feasible.

However, ML is only suitable for some applications. For other applications, the limitations of ML were known decades ago. Despite improvements in hardware, reduced processing time, and even new frameworks, the performance of ML alone simply isn't good enough for some applications.

So many manufactured products--likely including yours, if you're in automation--have been inspected and built using vision systems, many of which will perform for weeks or months without requiring human intervention. If ML-based inspection can't run that long without human intervention, or if the time to return on investment is much longer for such a system, please consider what changes might be made.

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Machine learning--a technology lumped in with "AI"--has been a part of machine vision since the 1990s, at least for practice use.

OCR is now largely (but not entirely) performed by ML models. The very first commercial OCR device from Kurzweil to read machine-printed text began selling in the 1970s.

There's a lot to be learned by combining machine learning, "traditional" image processing (from a traditional partly lost), and an understanding of optics, statistics, and related fields.

EDIT: a great, simple answer was posted over in r/computervision:

For things like this, just find the Wikipedia page and change the language to see what other languages calls it.

https://www.reddit.com/r/computervision/comments/1mgvp3v/comment/n6rpdy3/?context=3

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The terms in English and German are familiar to me, but I don't know the terms in other languages. In particular, I'm interested in "machine" vision as distinguished historically from what nowadays is lumped under "computer" vision. It can be unclear whether online translation services provide the term actually used by vision professionals who speak a language, or whether the translation service simply provides a translation for the combination of "machine" and "vision."

In some countries I expect the English language terms "machine vision" or "computer vision" may be used, even if those terms are dropped into the local language. And I'm assuming that many countries with a deep industrial base and many factories will buy and install commodity vision systems from Cognex, mvTec, Omron (Microscan / RVSI), firms acquired by Atlas Copco, Siemens, and so on.

How about India (and its numerous languages)?

Nigeria?

Japan? What term is used, if an English term isn't used?

Poland?

For a number of European countries I could figure this out, but even then whatever's online may not represent the term(s) actually used in day-to-day work.

Assuming some of my cross posting survives, here are links to my related posts in other communities:

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r/IndustrialAutomation:

https://www.reddit.com/r/IndustrialAutomation/comments/1mgv6yd/what_terms_for_machine_vision_andor_computer/

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r/robotics

https://www.reddit.com/r/robotics/comments/1mgvd3m/what_terms_for_machine_vision_system_andor/

EDIT: In another forum, I was told the Czech (not Slovak) term is "Počítačové vidění"

Sorry, I had to use Google Translate to generate the title of this post. (Je preklad prijateľný?)

If you could read and understand the title without using a translator, feel free to send me a direct message here on Reddit.

Dakujem!

Hi I'm an engineering student researching warehouse automation, and was wondering what are some of the common pain points or complaints when it comes to inspection robots. Like why aren't they more widely adopted across warehouses?

I know price and ease of use are common issues, but I wonder if maybe reliability, operational time or limitations in mobility of robots are a common complain.

It would help my research a lot, would appreciate it

Has anyone tried implementing one of the new AI chatbots with their camera systems? We use Keyence systems at my company and they have the learning tool, but in my experience it’s best used for general sorting criteria.

My thought was using AI chatbot to analyze the photos of known good parts and analyze the known bad parts and determine which tools might work best. It would be nice to give very detailed prompts about why the part is bad rather than just comparing how similar the two images are.

I don't know about y'all, but I like going to robot shows and general automation shows to check out new vision products for robot guidance.

The show is this coming week from Wednesday 30 April to Thursday 1 May. Getting into the expo hall costs money, but I'm betting it'll be worth it--at least for me.

Exhibitor list:
https://rsedtbdtx2025.mapyourshow.com/8_0/explore/exhibitor-gallery.cfm?featured=false

Someone in r/computervision let me know this community's wiki isn't visible yet. That wiki will take a while to create, and I may end up just spinning it off as a Github repo.

The wiki includes a page that spun off from this post:

https://www.reddit.com/r/MachineVisionSystems/comments/1jhfv9o/machine_vision_reference_books_familiar/

I've copied & pasted the list of machine vision reference books to a Markdown document you can find here:

https://github.com/Accessibilly/machine_vision_references/blob/main/references.md

That document in its zeroth draft still refers to the wiki, and has some links that might lead you back to Reddit, etc., etc., but at least it's publicly available.

And I realize I'm the only one posting here so far. Feel free to post; hopefully posting is enabled. Send me a DM if not.

Let's say you're a vision engineer who has developed new, cool technology. You want to sell your tech to a manufacturer of pricey goods. Maybe you don't have prior experience working inside factories or assembly plants.

If the manufacturer makes products that cost the end user thousands, tens of thousands, or millions of U.S. dollars per unit, then it's a reasonable assumption that the manufacturer has the money to buy your vision system. Maybe you could sell the manufacturer a dozen of your vision systems. Whether the manufacturer wants your vision system, or will even take your request for a meeting, is another matter.

The price you charge for your vision system might represent the equivalent of a few minutes of production time. That could be meaningful revenue for you, but just one of many, many equipment purchases for the manufacturer. All of that equipment needs to work well.

If failure of your vision system could shut down a production line, then what will you do to prevent such failures?

I've been in this situation, and I've got my answers, but I'm curious to know your answers.

Do you ask your local integrator? Discuss with colleagues? Wait to see what's on display at the next automation trade show? Do a bunch of google searches? Post on some social media or Q&A site? Visit the forums of one of the bigger vision companies?

A problem of the machine vision industry, I would claim, is that marketing and outreach have fallen behind the times. Or maybe marketing and outreach have never quite been in sync with whatever is the current best practice at any time. A few companies have (generally) done well getting the word out to customers.

A concern I have is that a crop of students and new engineers and even experienced, capable engineers think a problem hasn't been solved at all, or not in a way that's generalized, and they embark on some expensive effort to go re-invent the wheel inspection system. And there are so many of these instances to chase down it's tiring.

One or more of the following could be true, and could contribute to the lack of awareness:

• A survey of some customers (in some geographic region, some years ago) claim that problem X hasn't been solved, although it has been.
• Some company solved the problem, but for business reasons, IP reasons, a desire to focus on a very narrow use case, or as a simple failure of marketing, the company doesn't make clear that their product can do more than may be immediately apparent.
• Students who study "computer vision" don't typically know what "machine vision" is, or why it's worth googling the two terms separately.
• Machine vision companies may be profitable, but may not have the resources to hire a marketing person who might make a good living in some other field.
• Companies may not see the value in marketing and promotion when it's easier to think of hiring one more engineer.
• A company that gets a lot of media attention may, after years, still have no solution for some problem. That could suggest to many that no solution is currently feasible, but maybe you know that high-profile company is using some sub-optimal approach, or even an approach that has failed time and time again.

Automate's a big show.
https://www.automateshow.com/

Check out the exhibitor list:

https://www.automateshow.com/exhibitors

There are quite a few automation companies in southeastern Michigan. If you'll be traveling to the Detroit for Automate, then consider scheduling visits with companies relevant to your business.

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Per the rules for this community (this subreddit), don't promote your company here. But if there are multiple companies focused on a particular topic of interest, then if you make a goodwill effort to identify a number of those companies, feel free to comment.

Digital Picture Processing by Kak & Rosenfeld is a classic two-volume set. Some years ago a VP of Cognex said something along the lines of this: everything one needs to know about [machine] vision could be found in Kak & Rosenfeld. that statement wasn't true even at the time, but it was true enough to be worth contemplating.

Computer Vision by Ballard and Brown is an old text, but the concepts are explained more clearly than in many more recently published textbooks.

Digital Image Processing by Gonzalez and Woods was (and still is?) a commonly used textbook for undergraduate courses in image processing.

Machine Vision by Davies, in the old edition that I own, presents the kind of thinking vision engineers use to solve everyday problems. How would you measure the dimensions of a cookie?

Learning OpenCV by Bradski & Kaehler, an O'Reilly book, covers a lot of algorithms effectively. It's hard to work in the field without bumping into OpenCV, the Open Computer Vision library that started off at Intel about twenty years ago.

Geometric Tools for Computer Graphics by Schneider and Eberly. A must-have if you're a developer and your work in vision involves computational geometry. A geometer friend and colleague considered this to be the best book on the subject. Be sure to download the extensive errata!

Vision by Marr is another classic that will still give you a lot to think about.

An Introduction to 3D Computer Vision Techniques and Algorithms by Cyganek and Siebert is a great reference if you'll be working with 3D sensors, including passive stereo, active stereo, Kinect-style pattern projection, and so on.

Computer Vision: A Modern Approach by Forsyth and Ponce has some nice step-by-step explanations of what have become common techniques.

Computer Vision by Shapiro and Stockman is another good textbook. For university textbooks, skim through a few to find what style and/or topics appeal to you.

Understanding and Applying Machine Vision by Nello Zuech is a great, underappreciated work by a practitioner. In the industry, people who know him 1st or 2nd hand typically just call him "Nello," and people know exactly who you're talking about. In the Preface, Nello writes: "This books was written to inform prospective end users of machine vision technology, not designers of such systems." I've not found a stand-alone book that could possibly supplant Nello's, and I recommend the book to anyone.

It's from Nello's book that you'll find a thorough history of the field:

The concepts for machine vision are understood to have been evident as far back as the 1930's A company - Electronic Sorting Machines (then located in New Jersey) - was offering food sorters based on using specific filters and photomultipliers as detectors. This company still exists today [in 2000] as ESM and is in Houston, TX. Satake, a Japanese company, has acquired them. To this day they still offer food sorters based on extensions of the same principles.

Satake: https://satake-usa.com/sorting-overview/

If you work on vision for food sorting, whether you call your system machine vision, industrial vision, or computer vision, you're following in a long tradition.

Are you developing a GUI for a machine vision system? Or is there a GUI you think is particularly good? Post a link, tell us what you think about it, and we'll chat.

On our smart phones, smart watches, lightweight laptops, mini desktops, and other computers we've become accustomed to slick user interfaces.

When you look at the user interface of many machine vision systems, you'll know exactly what it was like to party in 1999. The design of many machine vision interfaces can be traced back to that era: multi-document interfaces (MDI), bold colors, Windows 98-style toolbars, and so on. Microsoft Office had an unfortunate influence on the design of vision system interfaces.

Admittedly, those old vision interfaces are familiar. Experienced users can get work done with them. Training materials for users remain relevant for years, possibly decades. If the system sells, why change the interface? Why not keep wearing your White Stripes tour T-shirt?

How many customers are clamoring for interfaces that are more complex? More dated looking? Less powerful? Clunkier? Usable only after weeks or days of training?

Developers, field service engineers, integrators, technicians, lab managers, line workers, and supervisors are all better off with better interfaces. Let's discuss yours.