1stVision Inc. - Machine Vision Articles - Page 4 of 51

May 24, 2024May 24, 2024

Lens extension tube or close up ring increases magnification

Summary at a glance:

Need a close-up image your preferred sensor and lens can’t quite deliver? A glass-free extension tube or close up ring can change the optics to your advantage.

C-mount extension tube kit – Courtesy Edmund Optics

What’s an extension tube?

An extension tube is a metal tube one positions between the lens and the camera mount. It comes with the appropriate threads for both the lens and camera mount, so mechanically it’s an easy drop-in procedure.

By moving the lens away from the optical plane, the magnification is increased. Sounds like magic! Well almost. A little optical calculation is required – or use of formulas or tables prepared by others. It’s not the case than any tube of any length will surely yield success – one needs to understand the optics or bring in an expert who does.

S-mount extension tube kit – Courtesy Edmund Optics

Note: One can also just purchase a specific length extension tube. We’ve shown images of kits to make it clear there are lots of possibilities. And some may want to own a kit in order to experiment.

Example

Sometimes an off-the-shelf lens matched to the sensor and camera you prefer suits your optical needs as well as your available space requirements. By available space we mean clearance from moving parts, or ability to embed inside an attractively sized housing. Lucky you.

But you might need more magnification than one lens offers, yet not as much as the next lens in the series. Or you want to move the camera and lens assembly closer to the target. Or both. Read on to see how extension rings at varying step sizes can achieve this.

Navigating the specifications

Once clear on the concept, it’s often possible to read the datasheets and accompanying documentation, to determine what size extension tube will deliver what results. Consider, for example, Moritex machine vision lenses. Drilling in on an arbitrary lens family, look at Moritex ML-U-SR Series 1.1″ Format Lenses, then, randomly, the ML-U1217SR-18C.

ML-U1217SR-18C 12mm lens optimized for 3.45um pixels and 12MP sensors – Courtesy Moritex

If you’ve clicked onto the page last linked above, you should see a PDF icon labeled “Close up ring list“. It’s a rather large table showing which extension tube lengths may be used with which members of the ML-U-SR lens series, to achieve what optical changes in the Field-Of-View (FOV). Here’s a small segment cropped from that table:

Field-Of-View changes with extension tubes of differing lengths – Courtesy Moritex

Compelling figures from the chart above:

Consider the f12mm lens in the rightmost column, and we’ll call out some highlights.

Extension tube length (mm)	WD (far)	Magnification
0	100	0.111x
2	58.2	0.164
5	13.5	0.414

5mm tube yields 86% closer WD and 4x magnification!

Drum roll here…

Let’s expand on that table caption above for emphasis. For this particular 12mm lens, by using a 5mm extension tube, we can move the camera 86% closer to the target than by using just the unaugmented lens. And we quadruple the magnification from 0.111x to 0.414x. If you are constrained to a tight space, whether for a one-off system, or while building systems you’ll resell at scale, those can be game-changing factors.

Any downside?

As is often the case with engineering and physics, there are tradeoffs one should be aware of. In particular:

The light reaching the focal plane is reduced, per the inverse square law – if you have sufficient light this may not have any negative consequences for you at all. But if pushed to the limit resolution can be impacted by diffraction.
Reduced depth of field – does the Z dimension have a lot of variance for your application? Is your application working with the center segment of the image or does it also look at the edge regions where field curvature and spherical aberrations may appear?

We do this

Our team are machine vision veterans, with backgrounds in optics, hardware, lighting, software, and systems integration. We take pride in helping our customers find the right solution – and they come back to us for project after project. You don’t have to get a graduate degree in optics – we’ve done that for you.

Give a brief idea of your application and we’ll provide options.

Related resources

You might also be interested in one or more of the following:

1st Vision’s sales engineers have over 100 years of combined experience to assist in your camera and components selection. With a large portfolio of cameras, lenses, cables, NIC cards and industrial computers, we can provide a full vision solution!

About you: We want to hear from you! We’ve built our brand on our know-how and like to educate the marketplace on imaging technology topics… What would you like to hear about?… Drop a line to info@1stvision.com with what topics you’d like to know more about.

May 24, 2024

Monochrome light better for machine vision than white light

Black and white vs. color sensor? Monochrome or polychrome light frequencies? Visible or non-visible frequencies? Machine vision systems builders have a lot of choices – and options!

Let’s suppose you are working in the visible spectrum. You recall the rule of thumb to favor monochrome over color sensors when doing measurement applications – for same sized sensors.

So you’ve got a monochrome sensor that’s responsive in the range 380 – 700 nm. You put a suitable lens on your camera matched to the resolution requirements and figure “How easy, I can just use white light!”. You might have sufficient ambient light. Or you need supplemental LED lighting and choose white, since your target and sensor appear fine in white light – why overthink it? – you think.

Think again – monochrome may be better

Polychromatic (white) light is comprised of all the colors of the ROYGBIV visible spectrum – red, orange, yellow, green, blue, indigo, and violet – including all the hues within each of those segments of the visible spectrum. We humans perceive it as simple white light, but glass lenses and CMOS sensor pixels see things a bit differently.

Chromatic aberration is not your friend

Unless you are building prisms intended to separate white light into its constituent color groups, you’d prefer a lens that performs “perfectly” to focus light from the image onto the sensor, without introducing any loss or distortion.

Lens performance in all its aspects is a worthwhile topic in its own right, but for purposes of this short article, let’s discuss chromatic aberration. The key point is that when light passes through a lens, it refracts (bends) differently in correlation with the wavelength. For “coarse” applications it may not be noticeable; but trace amounts of arsenic in one’s coffee might go unnoticed too – inquiring minds want to understand when it starts to matter.

Take a look at the following two-part illustration and subsequent remarks.

Transverse and longitudinal chromatic aberration – Courtesy Edmund Optics

In the illustrations above:

C denotes red light at 656 nm
d denotes yellow light at 587 nm
F denotes blue light at 486 nm

Figure 1, showing transverse chromatic aberration, shows us that differing refraction patterns by wavelength shift the focal point(s). If a given point on your imaged object reflect or emits light in two more more of the wavelengths, the focal point of one might land in a different sensor pixel than the other, creating blur and confusion on how to resolve the point. One wants the optical system to honor the real world geometry as closely as possible – we don’t want a scatter plot generated if a single point could be attained.

Figure 2 shows longitudinal chromatic aberration, which is another way of telling the same story. The minimum blur spot is the span between whatever outermost rays correspond to wavelengths occurring in a given imaging instance.

We could go deeper, beyond single lenses to compound lenses; dig into advanced optics and how lens designers try to mitigate for chromatic aberration (since some users indeed want or need polychromatic light). But that’s for another day. The point here is that chromatic aberration exists, and it’s best avoided if one can.

So what’s the solution?

The good news is that a very easy way to completely overcome chromatic aberration is to use a single monochromatic wavelength! If your target object reflects or emits a given wavelength, to which your sensor is responsive, the lens will refract the light from a given point very precisely, with no wavelength-induced shifts.

Making it real

The illustration below shows that certain materials reflect certain wavelengths. Utilize such known properties to generate contrast essential for machine vision applications.

Red light reflects well from gold, copper, and silver – Courtesy CCS Inc.

In the illustration we see that blue light reflects well from silver (Ag) but not from copper (Cu) nor gold (Ag). Whereas red light reflects well from all three elements. The moral of the story is to use a wavelength that’s matched to what your application is looking for.

Takeaway – in a nutshell

Per the carpenter’s guidance to “measure twice – cut once”, approach each new application thoughtfully to optimize outcomes:

Is the application best suited to visible or non-visible portions of the spectrum?
If visible, is it best solved with a color sensor or a monochrome sensor (possibly using a specific color frequency)?
Avoid chromatic aberration if possible by using monochromatic light
Consider light sources and filters, in addition to the characteristics of your application’s substances and scenes

Click to contact — Give us an idea of your application and we will contact with lighting options and suggestions.

Additional resources you may find helpful from 1stVision’s knowledge base and blog articles: (in no particular order)

April 23, 2024April 23, 2024

LWIR – Long Wave Infrared Imaging – Problems Solved

What applications challenges can LWIR solve?

LWIR is the acronym, is it reminds us where on the electromagnetic spectrum we’re focused – wavelengths around 8 – 14 micrometers (8,000 – 14,000 nm). More descriptive is the term “thermal imaging”, which tells us we’re sensing temperatures not with a contact thermometer – but using non-contact sensors detecting emitted or radiated heat.

Remember COVID? Pre-screening for fever. Courtesy Teledyne DALSA.

Security, medical, fire detection, and environmental monitoring are common applications. More on applications further below. But first…

How does an LWIR camera work?

Most readers probably come to thermal imaging with some prior knowledge or experience in visible imaging. Forget all that! Well not all of it.

For visible imaging using CMOS sensors, photons enter pixel wells and generate a voltage. The array of adjacent pixels are read out as a digital representation of the scene passed through the lens and onto the sensor, according to the optics of the lens and the resolution of the sensor. Thermal camera sensors work differently!

Thermal cameras use a sensor that’s a microbolometer. The helpful part of the analogy to a CMOS sensor is there we still have an array of pixels, which determines the resolution of the camera, as a 2D digital representation of the scene’s thermal characteristics.

But unlike a CMOS sensor whose pixels react to photons, a microbolometers upper pixel surface, the detector, is comprised of IR absorbing material, such as Vanadium oxide. The detector is heated by the IR exposure, and the intensity of exposure in turn changes the electrical resistance. The change in electrical resistance is measured and passed by an electrode to a silicon substrate and readout integrated circuit.

Vanadium oxide (VOx) pixel structure – Courtesy Teledyne DALSA

Just as with visible imaging, for machine vision it’s the digital representation of the scene that matters, as it’s algorithms “consuming” the image in order to take some action: danger vs. safe; good part vs. bad part; steer left, straight, or right – or brake; etc. Whether one generates a pseudo-image for human consumption may well be unnecessary – or at least secondary.

Applications in LWIR

Applications include but are not limited to:

Security e.g. intrusion detection
Health screening e.g. sensing who has a fever
Fire detection – detect heat from early combustion before smoke is detectable
Building heat loss – for energy management and insulation planning
Equipment monitoring e.g. heat signature may reveal worn bearings or need for lubrication
Food safety – monitor whether required cooking temperatures attained before serving

You get the idea – if the thing you care about generates a heat signature distinct from the other things around it, thermal imaging may be just the thing.

What if I wanted to buy an LWIR camera?

We could help you with that. Does your application’s thermal range lie between -25C and +125C? Would a frame rate of 30fps do the job? Does a GigEVision interface appeal?

It’s likely we’d guide you to Teledyne DALSA’s Calibir GX cameras.

Calibir GX front and rear views – Courtesy Teledyne DALSA

Precision of Teledyne DALSA Calibir GX cameras

Per factory calibration, one already gets precision to +/- 3 degrees Celsius. For more precision, use a black body radiator and manage your own calibration to +/- 0.5 degrees Celsius!

Thresholding with LUT

Sometimes one wants to emphasize only regions meeting certain criteria – in this case heat-based criteria. Consider the following image:

Everything between 38 and 41°C shown as red – Courtesy Teledyne DALSA

Teledyne DALSA Calibir GX control software let’s users define their own lookup tables (LUTs). One may optionally show regions meeting certain temperatures in color, leaving the rest of the image in monochrome.

Dynamic range

The “expressive power” of a camera is characterized by dynamic range. Just as the singers Enrico Caruso (opera) and Freddie Mercury (rock) were lauded for their range as well as their precision, in imaging we value dynamic range. Consider the image below of an electric heater element:

“Them” (left) vs. us (right) – Courtesy Teledyne DALSA

The left side of the image if from a 3rd party thermal imager – it’s pretty crude essentially showing just hot vs. not-hot, with no continuum. The right side was obtained with a Teledyne DALSA Calibir GX – there we see very hot, hot, warm, slightly warm, and cool – a helpfully nuanced range. Enabled by a 21 bit ADC, the Teledyne DALSA Calibir GX is capable of a dynamic range across 1500°C.

In this short blog we’ve called out just a few of the available features – call us at 978-474-0044 to tell us more about your application goals, and we can guide you to whichever hardware and software capabilities may be most helpful for you.

April 23, 2024April 23, 2024

Artificial intelligence in machine vision – today

This is not some blue-sky puff piece about how AI may one day be better / faster / cheaper at doing almost anything at least in certain domains of expertise. This is about how AI is already better / faster / cheaper at doing certain things in the field of machine vision – today.

Classification of screw threads via AI – Courtesy Teledyne DALSA

Conventional machine vision

There are classical machine vision tools and methods, like edge detection, for which AI has nothing new to add. If the edge detection algorithm is working fine as programmed in your vision software, who needs AI? If it ain’t broke, don’t fix it. Presence / absence detection, 3D height calculation, and many other imaging techniques work just fine without AI. Fair enough.

From image processing to image recognition

As any branch of human activity evolves, the fundamental building blocks serve as foundations for higher-order operations that bring more value. Civil engineers build bridges, confident the underlying physics and materials science lets them choose among arch, suspension, cantilever, or cable-stayed designs.

So too with machine vision. As the field matures, value-added applications can be created by moving up the chunking level. The low-level tools still include edge-detection, for example, but we’d like to create application-level capabilities that solve problems without us having to tediously program up from the feature-detection level.

Traditional methods (left) vs. AI classification (right) – Courtesy Teledyne DALSA

Traditional Machine Vision Tools	AI Classification Algorithm
– Can’t discern surface damage vs water droplets	– Ignores water droplets
– Are challenged by shading and perspective changes	– Invariant to surface changes and perspective

For the application images above, AI works better than traditional methods – Courtesy Teledyne DALSA

Briefly in the human cognition realm

Let’s tee this up with a scenario from human image recognition. Suppose you are driving your car along a quiet residential street. Up ahead you see a child run from a yard, across the sidewalk, and into the street.

While it may well be that the rods and cones in your retina, and your visual cortex, and your brain used edge detection to process contrasting image segments to arrive at “biped mammal” – child, , and on to evaluating risk and hitting the brakes – isn’t how we usually talk about defensive driving. We just think in terms of accident avoidance, situational awareness, and braking/swerving – at a very high level.

Applications that behave intelligently

That’s how we increasingly would like our imaging applications to behave – intelligently and at a high level. We’re not claiming it’s “human equivalent” intelligence, or that the AI method is the same as the human method. All we’re saying is that AI, when well-managed and tested, has become a branch of engineering that can deliver effective results.

So as autonomous vehicles come to market of course we want to be sure sufficient testing and certification is completed, as a matter of safety. But whether the safe-driving outcome is based on “AI” or “vision engineering”, or the melding of the two, what matters is the continuous sequence of system outputs like: “reduce following distance”, “swerve left 30 degrees”, and “brake hard”.

Neural Networks

One branch of AI, neural networks, has proven effective in many “recognition” and categorization applications. Is the thing being imaged an example of what we’re looking for, or can it be dismissed? If it is the sort of thing we’re looking for, is it of sub-type x, y, or z? “Good” item – retain. “Bad” item – reject. You get the idea.

From training to inference

With neural networks, instead of programming algorithms at a granular feature analysis level, one trains the network. Training may include showing “good” vs. “bad” images – without having to articulate what makes them good or bad – and letting the network infer the essential characteristics. In fact it’s sometimes possible to train only with “good” examples – in which case anomaly detection flags production images that deviate from the trained pool of good ones.

Deep Neural Network (DNN) example – Courtesy Teledyne DALSA

Enough theory – what products actually do this?

Teledyne DALSA Astrocyte software creates a deep neural network to perform a desired task. More accurately – Astrocyte provides a graphical user interface (GUI) and a neural network framework, such that an application-specific neural network can be developed by training it on sample images. With a suitable collection of images, Teledyne DALSA Astrocyte can create an effective AI model in under 10 minutes!

Gather images, Train the network, Deploy – Courtesy Teledyne DALSA

Mix and match tools

In the diagram above, we show an “all DALSA” tools view, for those who may already have expertise in either Sapera or Sherlock SDKs. But one can mix and match. Images may alternatively be acquired with third party tools – paid or open source. And one may not need rules-based processing beyond the neural network. Astrocyte builds the neural network at the heart of the application.

User-friendly AI

The key value proposition with Teledyne DALSA Astrocyte is that it’s user-friendly AI. The GUI used to configure the training and to validate the model requires no programming. And one doesn’t need special training in AI. Sure, it’s worth reading about the deep learning architectures supported. They include: Classification, Anomaly Detection, Object Detection, and Segmentation. And you’ll want to understand how the training and validation work. It’s powerful – it’s built by Teledyne DALSA’s software engineers standing on the shoulders of neural network researchers – but you don’t have to be a rocket scientist to add value in your field of work.

1stVision Inc. – Machine Vision Articles

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