Info on code verification

Yes I know this is starting to look like BarCodes4Users and not MachineVision4Users, but I am fed up with trying to educate folks about this topic. So from now on I’m going to send everyone to “Verification” on the Microscan website. You’ll find links there to a ton (or tonne for my readers in Europe) of info. Please read it.

And yes, Cognex is big in code reading too. Real big. So if you’re shopping for hardware and software you owe it to yourself to check out both.

You’re a bunch of players

Having discussed the pros and cons of vision system standardization, (“Do you play the field?” November 14^th,) I conducted a poll to find out just how fickle you lot are.

The answer is, very.

Less than a third of you use only a single make or brand of vision system, and some 40% have three or more different brands. This is interesting, and perhaps a little disappointing.

To be blunt, it says to me that you buy what the salesman pushes. When the guy from Keyence shows you their latest-and-greatest, you buy it. And the same for Cognex. And Microscan. And Banner. And so on. I may be wrong on this, though I doubt it. And it’s a pity. You’re giving yourself substantial learning curves, spares, and support issues and making life more complicated than it needs to be.

Now the number of respondents was pretty small, so I may be being unfair on some of you, but really, three or more different brands? Next time you’re shopping don’t just believe the salesman, ask me instead.

Advantages of machine vision in reading bar codes

Reading codes, 1D and 2D, is a large and growing machine vision application area, yet there are still those who say laser scanners are sufficient. If that’s you, or maybe you’re just on the fence, (The “why use more tech than you need,” mindset,) take a look at “Image-Based ID Boosts Read Rates” (Control Design magazine, July 9^th, 2013.)

This gives a lot of detail about how the Cognex Dataman sensors do their job, but what I found more interesting was the explanation of the business benefits of higher read rates. Now if you’re at the grocery store checkout poor read rates are irritating, but in manufacturing they can be expensive, which is what the article describes. If justifying the upgrade to vision is giving you a headache, this article may offer some pointers.

And in case you think I’m plugging Cognex exclusively, let me remind you that Microscan also offers a very capable range of code reading products.

Machine vision in traceability systems

Cognex and Microscan both talk a lot about their code reading solutions. What I’ve not seen though is much explanation of why and how you’d want to deploy this technology. Now if you’re in the pharmaceutical industry you probably know this stuff, but if not, I suggest you take a look at the “What is Pharma Track-n-Trace” on the website of Omega Design Corporation.

In particular, take a look at two of the videos linked from that page. Start with “OmegaTrack Demo at PEI 2010. Here the President of Omega takes the viewer through a packaging line, pointing out the various vision systems employed.

Two systems on that line caught my eye. The first is the LabelSync Vision Station. This uses the Cognex OmniView tool to image the full 360 of the surface of a tablet container. A fifth camera reads the datamatrix code printed on the bottom of the container and software compares it with the code on the label.

The second system is at the end of the line, and unfortunately is not discussed in any detail. Again, it’s a multi camera system checking that all the codes are correct. What intrigued me though is the container codes are read through the clear plastic packaging. If you’ve ever attempted that task you’ll know it’s decidedly non-trivial. I’d love to know what lighting they use – would polarization have a part to play?

So there you have it: machine vision, actually it’s Cognex machine vision, in a track-and-trace application. I’m just curious why I haven’t seen it in any Cognex marketing material.

Ensuring bar codes are legible

If you’ve ever waited in line while a cashier tried repeatedly to scan a bar code you’ll know the importance of bar code legibility. Now take that situation and apply it to a medical or pharmaceutical setting. The consequences of a misread code could be devastating. That’s why we have code verification.

I find a lot of confusion with regard to checking bar codes. Many engineers seem to think it’s enough to check that a just-marked or applied code can be read. That’s what Microscan in their excellent white paper, “Understanding Machine Vision Verification of 1D and 2D Barcodes” call “validation” or “process control”.

That may be sufficient if you’re in an environment where compliance with bar code standards is not required, but in many industries it’s essential that you demonstrate an ability to meet the standards for legibility. That’s called “verification” and the Microscan paper will tell you how to do it. Here’s a link to the White papers section of their website: http://www.microscan.com/en-us/trainingandresources/machinevision.aspx#wp

While there, I suggest you look over their other papers. They have a lot of excellent training material, and it’s all free. You could even read it on your phone while standing in line at the checkout!

Patent wars

Cognex is going after Microscan, claiming its Mobile Hawk DataMatrix codereader infringes a Cognex patent. (Yahoo Finance, April 2^nd, 2013.) Unsurprisingly, Microscan asserts that Cognex’s claim is “without merit.”

One wonders what’s really going on. Could Microscan really have stolen Cognex’s ideas and put them out in the Hawk?

Seems unlikely to me. Cognex is a pretty litigious company and they like to protect their intellectual property. It looks to me more like a shot across the bows. By forcing Microscan to spend a lot of time and energy defending themselves Cognex is putting them on notice that they will not give up market share without a very expensive fight.

Can Microscan afford that fight? I suspect not, so eventually some deal will be worked out. My bet is that Cognex will agree to go away if Microscan stays off their turf.

Perhaps Microscan should be flattered that Cognex is taking them seriously!

Does your task need OCV or OCR?

If you looked at the Microscan video showcasing the latest release of their AutoVISION package, (“Looking for an easy-to-use smart camera?”) you might have spotted that it includes both Optical Character Verification (OCV) and Optical Character Recognition (OCR.)

I’ve noticed that some of our less experienced machine vision brethren see these two as interchangeable, when they are not, so I thought a short post on the difference might be useful. (If this is “old hat” to you, go find some amusing videos on YouTube and check back here in a day or two.)

Text is great for humans because we’re really good at pattern recognition. In fact it may be the only visual area in which we can beat machine vision. Nate Silver would argue that we’re too good at it. So when we see marks on a page we can quickly interpret them as characters. Software doesn’t know characters though; it just sees groups of pixels of different gray levels.

So vision software treats characters as shapes, or patterns, and OCV is a form of pattern matching task. The algorithm just needs to determine how closely the pattern in the ROI matches the pattern stored in memory.

Allowances can be made for scale and rotation, but in general, if the degree of match exceeds a threshold the system will say, “I was looking for shape T and the shape in the ROI passes the tests for T, therefore I have found T.” The character has been verified.

OCR is more complicated. In this task the software has to ask, “I have found a shape. Which of the shapes stored in memory does it match most closely?”

See how this is more complicated? There’s a whole lot more processing to be done, especially if size and rotation are variable too.

So when you need to recognize characters, try to configure the task as a verification – “I’m looking for T. How closely does this shape match?” – rather than “what is this shape?” OCV is always going to be easier/faster/more robust than OCR.

Looking for an easy-to-use smart camera?

Microscan emailed me recently to let me know they’ve updated their AutoVISION machine vision software. There’s a good introductory video on their website, and if you’re in the market for a smart camera that can be put to work quickly you might want to take a look.

As they video makes clear, you’re not going to do a lot of fancy image processing with this. But if you have some presence-absence checks to make, maybe some OCV or OCR, or some code-reading, then this is worth considering.

The video is mercifully brief, (I have such short attention span these days,) but in consequence it skips over the small matter of lighting and lens selection. It also skimps on how the inspection program gets from PC to smart camera, but that’s good because it gives me fodder for a post or two.

I should mention that Microscan do have some good educational videos on their website; just look under the Training tab on their home page.

Lighting is job one

All those fancy vision tools in your favorite package, none of them inspect the objects passing down your conveyor. All they do is perform measurements on the image the camera acquired. So if the image is not a reliable representation of reality the results will be pretty meaningless.

You vision experts know what I’m driving at: the image is everything and it all starts with lighting that maximizes the signal to noise. In this case the signal is the features you want to detect or measure and the noise is all the other clutter.

We’ve learnt a lot about lighting over the years but sometimes it’s still good to be reminded of the basics, and it used to be that the best material on lighting came from Nerlite. Well Nerlite was absorbed into Microscan long ago, but their material still exists, and has recently been reissued in the pages of Assembly. The article title, “Eight Tips for Optimal Machine Vision Lighting” sums it up nicely.

Lens focusing, simplified?

Anything has to be better than turning the focus ring first one way, then the other while trying to watch an image on a monitor. It’s even worse when, as in a system I worked on recently, the monitor is not viewable when working at the camera. And don’t get me started on the tribulations of linescan camera focusing!

All of which is why, since 2009 I’ve been getting excited about the potential of liquid lenses. (“The end of focus problems” June 14^th, 2009.) Cognex and Microscan have offered liquid lenses on select products for a few years, but there’s been nothing I could buy to add to a camera, until now.

Just in time for Vision 2012 optics specialists Qioptiq have announced their flo.x lens “with liquid lens focusing.” This sounds exactly what I’ve been waiting for, albeit with a couple of drawbacks. First, it’s made for an M12 mount, rather than C-mount. And second, the focal length is a rather wide angle 3.35mm.

No, it’s not exactly what I’ve been waiting for, but the very fact that it exists gives me hope. Who knows, perhaps I’ll be sent a plane ticket to Stuttgart so I can attend Vision 2013 for the unveiling of the C-mount liquid lens!

By-the-way, if you’re looking on the Qiotiq website for details of the flo.x, - well I couldn’t find anything, although they do have some great machine vision lenses.

Machine Vision Education

Perhaps you stumbled across this site because you need to learn about machine vision. If so, allow me to point you at some quality video material. This comes from Microscan, who sell cameras, lights, and software. In short, they’re positioning themselves as something of a one-stop-shop for vision.

Needless to say, though I shall say it anyway, their videos are a little biased towards the use of Microscan products, but I’ve found them a pretty good start point. They won’t get you through the AIA’s Certification but they’ll put you on the right road.

Take a look and come back to me with questions.

Cognex results for 2011

I try to be one of the first to bring you financial results from machine vision industry leader Cognex, but last week I got scooped. It was technology industry blog Virtual Strategy that altered me to news of record results from our friends in Natick, MA..

Click that link for details, or go to www.cognex.com, but if you’re interested in my summation and interpretation, read on.

First, the headlines. Quarterly comparisons are a little misleading because of a one-off adjustment at the end of 2010, so let’s look at the twelve month numbers: revenues (sales, for my British readers,) up 11% to almost $322m, and income (profit, in British English,) up 14% to $61m. Clearly then, marginal growth in sales volumes translates to lots more profit. I think that means high fixed costs, and a high margin per product sold.

What’s missing however is any discussion of units sold. That’s something I’d like to see because it’s my understanding that Cognex pushed through a pretty steep price increase last year. So when CEO Bob Willet says that revenue for Q1-12 is expected to be flat, doesn’t that mean volumes are actually declining?

Now the business of business is to make profit, so if they can make more money by shifting fewer units, that’s a good thing, isn’t it? Well perhaps it’s more complicated than that.

What I think is happening, and this is pure conjecture, is that the sales mix is shifting towards Dataman code readers and Checker sensors. This lowers the average revenue per unit sold, but it also boosts profit since these are high margin products made in reasonably big volumes. It would also imply that perhaps the VisionPro line is going to diminish in importance, leaving Cognex to go head-to-head with Microscan at the less sophisticated end of the vision market.

And one other observation before you get on with your day: I spotted a few hints in the year-end report about new product introductions. There’s the increase in inventories, the growth in R&D headcount, and the expansion of SGA costs. Is something exciting about to be launched?

It would be good to see something innovative from Cognex, although I’m not holding my breath.

Feedback on evaluating LED lights

Back in November I posted a few words on wisdom on how I go about assessing the quality and performance of LED machine vision lights from the many vendors out in the marketplace. The point I was trying to make was that most of us lack any sophisticated test equipment, so instead we have to make do with a few more subject methods.

Well the guys at Microscan took the time to write a detailed comment to my post, but as I’m not sure how many readers will actually go back to look at that article I thought I’d take the liberty of reproducing their comments here.

Great blog post! I asked our NERLITE veteran engineers for their perspective; here is their reply:

While it is a fact that the cooler a particular LED is the longer it will last, this is much too broad of a generalization. Different LEDs have a very wide range of maximum operating temperatures. If the criteria is “hot to the touch”, some LEDs may already be past their limits while others might not be even close. This used to be a good "rule of thumb" in the early days of LED illumination, but devices have significantly evolved and heat does not always mean a bad design. For example, Microscan Nerlite’s new Smart Series DOALs run hotter than our previous design and are warmer to the touch, but the LED's are robust enough that we were able to increase intensity, improve uniformity, eliminate cooling fins and operate the unit at a 10 degree C greater maximum ambient temperature.

We agree [regarding the need for good mechanical rigidity and mounting points]. For example with the Smart Series product line, we were able to take into consideration feedback from application engineers and customers to incorporate more common mounting points while maintaining backward mounting compatibility to prior product. Each design was subjected to shock and vibration testing to ensure mechanical stability in the field and we also took into consideration IP ratings that are common to each product family. In terms of cleaning, it is important to note all units with beam splitters, regardless of manufacturer, have special cleaning requirements. This is due to the delicate nature of the coatings used on the beam splitters.

We agree [regarding the importance of LED placement uniformity]. If the LEDs are not positioned uniformly, the output will not be uniform either. With non-diffuse, focused LED arrays, the trueness of LED placement makes a big difference and dictates uniformity of light coverage. For example when Nerlite products require specific LED alignment, we either incorporate the alignment feature into the design, or specifically design alignment tools to be used at the time of assembly.

[On how to assess uniformity] For non-diffuse, short working distance area arrays and ring lights, this is basically true. [Small LEDs are to be preferred over big ones.] It does not take into account the output angle of the LEDs’ optics. Even packed tightly together, an LED with a very narrow output angle can still be spotty. LEDs with a wide output pattern can be more uniform. This is true when considering the uniformity of non-diffuse, focused LED arrays, but when it comes down to having a truly diffuse light source, seeing "any" type of LED is not a good sign. Nerlite has applied many techniques, often patent protected, to achieve superior uniformity regardless of LED size or quantity. One final note is that for area arrays designed for long working distances (Hi-Brites), the overlap that occurs over the long distance makes LED density less of an issue.

[Assessing value-for-money – I suggested calculating the price per watt.] This may be true when comparing "apples to apples", but Machine Vision Illumination has entered a new era where more technical features are designed into the product itself. This simplistic approach also does not take into account the quality and integrity of construction, IP rating, optical design, efficiency of the electronics and optics, ease of use (mounting options, accessories, etc.), warranty, and so on. Even if all other factors are equal, some LEDs are more efficient than others. For example, the new smart rings use about the same amount of power as their predecessors, yet they contain 25-33% more LEDs and have 3-5X the light output depending on the model. Since the price and power consumption is about the same, by this criteria, one would judge the old and new units to be equal. This is, of course, totally false. Whether it's just simple circuit protection or the comprehensive control features built into Smart Series Illuminators, there are vast differences and sometimes you truly do get what you pay for.

This is great feedback, although I’m not sure how many of these are ‘actionable points’ that can be applied by the machine vision end-user. I think my main point is still valid: it’s really difficult to tell what makes one light worth double the price of an identically-sized product.

Keep those comments coming!

Machine vision acquisitions news

PPT Vision, despite being a pioneer in the machine vision field, has been struggling for a long time. It’s probably good news then that a White Knight, in the form of Datalogic S.p.A. of Italy has ridden to their rescue.

According to the press release on the Datalogic website, they are paying $5.2m for a business with revenues of $6m. That ratio – 86% of revenue – tells us that PPT was not doing well, despite their market reputation, an enviable patent portfolio, and a recently rejuvenated product line-up.

But what does this move tell us about the state of the machine vision industry?

Well to answer that. Let’s take a look first at Datalogic. This is a company with 2010 revenues of €393m (around $515m), making them bigger than Cognex, that is concentrated in code marking and scanning, with related ventures in machine vision (using rebranded products, I believe,) and vision-based retail loss prevention (Evolution Robotics.)

The PPT acquisition comes hot on the heels of Datalogic’s deal to buy code printing and reading company Accu-Sort for $135m (147% of revenues.) (Press release here.) Clearly then, as the Italians say in the PPT announcement, they see “interesting growth prospects” in machine vision.

Implications?

Well, Datalogic are now going head-to-head with Microscan in the code reading and machine vision field. It also means that Cognex is no longer the undisputed heavyweight champion, though their profitability and cash reserves are second-to-none.

Will this prompt a further urge to merge? In the machine vision camera world we’ve already seen AVT parent Augusta snapping up manufacturers with complementary product portfolios, and then there was Dalsa’s acquisition by Teledyne, so I think the answer is yes, expect more mergers and acquisitions.

As for me, I’m going to start a vision company and then sell out to one of these big players.

Lighting tips

There are times when each of us struggles to create good contrast in our vision applications, so as a public service I’d like to share “Eight Tips for Optimal Machine Vision Lighting.” The tips are from Microscan and the article was published on the Quality Digest website, July 11^th, 2011.

The tips provided are all good, and they may well help inspire you, but I think their history or origin is also interesting. Those of you who’ve been around vision systems for a few years will recognize these as the same tips that NER used to offer. That shouldn’t be too surprising because NER was purchased by Microscan some time ago.

I wasn’t quite sure about the logic behind the acquisition, and for some time the lighting products seemed to languish, with no Champion to promote them in the vision marketplace. Perhaps this recent bit of PR is a sign that Microscan lighting is beginning to stir. That has to mean competition for Advanced Illumination, CCS and the other players, and competition is always good.

Update on Microscan

I’m quite impressed by the way in which Microscan is growing its family of machine vision products. Having started out as a code reading company, they acquired the old NER business from Siemens a while back and since then have put together an interesting portfolio.

This month they’ve announced “Three New Machine Vision Innovations” (their words, not mine,) so let’s take a look.

The “innovations” they’re talking about are the Vision Hawk and Vision Mini smart cameras and a new programming/configuration software tool, AutoVISION™. Now you may say there’s nothing new about a smart camera, but it’s fair to say that these are a little different. What Microscan has done is incorporate an autofocus lens into each of the smart cameras. That’s a nice touch because it moves these a little closer to being plug ‘n’ play devices.

You may recall that Cognex have a similar autofocus capability (they use a liquid lens design) in their Dataman code readers, but somewhat to my surprise, they haven’t yet rolled that out to the Checker vision sensors. This could be because they’re not happy with the image quality, though having used a few Checkers, (which I do like,) I’d have to say that the current lens is no great shakes. But perhaps there’s a different explanation. Maybe Microscan has a more rapid product development process.

Turning now to the new Microscan vision software; I haven’t got my hands on this yet but from the literature it appears somewhat reminiscent of the old DVT Framework product. Microscan point out that an application developed in AutoVISION can be moved onto the higher-end Visionscape platform, which is a good thing (try doing that with a Checker!) It also leads me to think that AutoVISION is not so much new as a scaled back version of Visionscape with a user-friendly GUI.

All in all, these are some interesting developments from a company that seems very serious about growing its machine vision business. If I worked from a certain Natick-based vision company I’d be watching Microscan like a hawk!

Crying out for Datamatrix code readers

Normal practice in this blog is to share interesting cases of where people have put machine vision to work. Today I’m going to do it slightly differently: I’m going to show you a situation where they should be using machine vision.

“Laser coding plastic fuel tanks,” (published on the OptoIQ website, May 1^st, 2011,) describes an automotive laser marking application. In this case a fiber laser is being used to etch a regular 1-D (like vertical fence posts) barcode on a black plastic gas tank. All well and good, until one get’s to the part that says, “… 2D would be more reliable, but they would have to change production line scanner controllers, which are not 2D capable.”

So rather than adopt a more robust means of encoding data, which would let them write more data and potentially improve traceability of what is a safety-critical component, the company prefers to stick with their old 1-D scanners. That seems a strange decision. Why not be proactive and offer the customers 2D coding? I know the auto industry and I know the big players are moving towards ever tighter traceability (not quite pharma level, but heading that direction.) Come to that, I thought the AIAG was pushing for greater adoption of 2D coding. Isn’t that why they have standards for code reading and verification?

Cognex, Microscan, call this company and tell them what 2D code readers can do.

A new option for reading codes

When I’m shopping for something to read 2D and DataMatrix codes I will admit that I tend to go straight to Cognex and their Dataman readers. I know that’s unfair to Microscan who also have a first rate portfolio of image-based code readers, but frankly I see little to differentiate the competing products so I just go with what I know works.

And now, along comes SICK with another product I can ignore.

The LECTOR®620 appears to be a pretty capable product, as far as I can tell by the video presentation on SICK’s web site. I do like the laser pointer for alignment; that seems a very neat way of speeding up installation and I wonder why other companies don’t offer the same. No word on pricing but I have to think it slightly undercuts the Dataman and Microscan products. Curious name though. Did no one at SICK ever see Silence of the Lambs?


On a broader note, I find it interesting to observe how SICK are growing their machine vision presence. It started with the acquisition of the IVP Ranger 3D family, then came the Inspector smart cameras/vision sensors, and now code readers. It’s as if they’re starting at the top and working down. Perhaps I need to stop ignoring this growing family of vision products.

A really cheap data matrix code reader

Code readers, from companies like Microscan and Cognex, are not that expensive, but if $2,000 or so breaks the bank, why not build your own?

Well if your time is free it might be worth doing, otherwise I’m not sure I see the point, but there is an army of robotics hobbyists out there who are fired up by the challenge of doing clever things with opensource software, so guys, this is for you.

In “How to Build an Open Source Vision Sensor” (Vision & Sensors, September 1, 2010) Anthony Oliver sets out a step-by-step tutorial. I’m not one of those Linux programming enthusiasts, but this short paper has almost inspired me to give it a go. If you’re curious about developing machine vision without spending a ton of money with the big vendors, and you have a little time on your hands, why not give it a try?

I’m curious to hear how you get on, so don’t forget to email me pictures and comments!

Microscan on YouTube

I don’t want it seem like I’m plugging Microscan – they’re paying me nothing for this publicity – but building on my previous post about their lighting products, I’d like to draw your attention to their YouTube presence.

As an inherently visual technology, I’m surprised machine vision companies don’t make more use of YouTube for marketing and education. The Microscan movies don’t exactly take my breath away – I’d really like to see more “How to…” information - but they’re a start. Here’s an example of what I mean, and this relates to my previous post on their “Hi-Brite” lighting family.



If you look through a list of their movies – more than 50 – you’ll see the views are very low. That’s a pity because this is such a great way of getting the word out. Join me in encouraging other machine vision vendors to make more use of YouTube.