Whether it’s in retail, robotics, or quality inspection, our customers today are finding ways to leverage AI to merge multiple different components at once instead of having a single line for one component.

While the attention of the masses may gravitate toward robots and AI, machine vision remains an indispensable and increasingly vital technology for businesses of all types around the world. In this episode of Manufacturing Matters, Jason Covar and Jeremy Bergh of The Imaging Source joined TECH B2B Marketing’s Winn Hardin and Jimmy Carroll to discuss a range of different machine vision topics, including the advancement of edge computing and edge AI, the difference between computer vision and machine vision, and the somewhat nebulous realm of embedded vision. Additional topics include cybersecurity, digital twins, the complementary role of AI within machine vision, and more.

Jimmy Carroll: [00:00:06] Hi everybody. My name is Jimmy Carroll. I’m the vice president of operations at Tech B2B Marketing. And welcome to this episode of the “Manufacturing Matters” podcast, where we discuss the latest trends and technologies reshaping the manufacturing industry today. Today we are at the A3 Business Forum in Orlando, and I have the pleasure of being joined by my friend and colleague Winn Hardin, along with Jason Covar and Jeremy Bergh of The Imaging Source. Guys, thanks so much for taking the time. Really appreciate it.

 

Jeremy Bergh: [00:00:31] Absolutely. Thanks for having us.

 

Jimmy Carroll: [00:00:33] It’s always a pleasure to see you guys. So I guess I’d start by —— for those who don’t know —— tell us a little bit about The Imaging Source and what you guys are excited about today.

 

Jason Covar: [00:00:43] Yeah, absolutely. Thanks again for the opportunity. The Imaging Source, for those that don’t know, are more than three decades into the industry, we’re a machine vision camera manufacturer. Headquarters is in Bremen, Germany. We have an office in Taipei, Taiwan, and then the U.S. presence, our headquarters is in Charlotte, North Carolina. And in the US, sales and support only.

 

Winn Hardin: [00:01:05] Outstanding. That kind of footprint explains why you’ve got such a diversity of camera offerings, I got a feeling from board and embedded systems —— we’ll talk more about that. But, Jeremy, was there anything else you wanted to add? I know you’re kind of a new addition, man.

 

Jeremy Bergh: [00:01:16] Yeah, I’m fairly new. I’ve been with them since October, so yeah, a little over three months. And you know, so far, so good. I really enjoy the company and the culture and Jason’s a great head of the U.S. And so it’s good being able to team up with him.

 

Winn Hardin: [00:01:33] Awesome. So let us know when you guys need us to go with you over to Oktoberfest.

 

Jeremy Bergh: [00:01:37] Will do.

 

Winn Hardin: [00:01:38] I’ll keep September open for us. We’re willing to do that.

 

Jimmy Carroll: [00:01:42] Yeah. And Jeremy, it’s like you’re new, but not really new. So it’s great to have you back at —— actually you didn’t miss the Forum, I don’t think, last year, right?

 

Jeremy Bergh: [00:01:50] No, I came but in a different capacity.

 

Jimmy Carroll: [00:01:53] Well it’s great to see you as always. So one of the things I wanted to ask you about, and we tend to talk about a lot in these podcasts, is the evolution of different technologies, whether that’s overall industrial automation, robotics, machine vision. In this case, I want to ask about machine vision obviously, given your focus. What are some of the most significant developments you’ve seen in the past several years in the machine vision space?

 

Jeremy Bergh: [00:02:19] Let’s see, I would say just the evolution of the chips, the technology on the different sensors and the chips and, you know, bringing processing to the edge. Obviously everyone talks about AI nowadays, but, you know, the machine learning algorithms and everything you can have, either on board or at the edge or very close so that you don’t have to run so much data. Especially as the cameras get higher resolution, you don’t want to have to bring that data so far back to the cloud or back to a server area. Sometimes it’s nice to be able to process that information right at the edge. So it’s been pretty interesting to see that evolution.

 

Jason Covar: [00:03:01] Yeah. Agreed. So I’ve been in for 11 years now, going on 12 in industry. And so we’ve seen the wave of hardware with software, hardware and software together. And I think that evolution you’re starting to see come back and swing towards kind of a complete system together. One thing I consider often is, you know —— backing out a little bit and looking at the computer vision part of it. So obviously machine vision is the mainstream focus for all of our companies. But as a manufacturer of hardware, I think it is important for us to also look at the opportunities with computer vision. And I think some of the reason it’s not is because it’s being developed. So we’re having to put things together. You know, there’re not standards for this technology today, and so it’s being developed on the fly and then dynamically. But certainly if you look at the numbers and expectation, you know, going out to 2030, 2033, it’s a big part for all of us, so we have to be considerate of that. And so we’re also developing right now. And so I think the last decade has helped us learn a lot. But I think this is where the future is.

 

Winn Hardin: [00:04:09] I got to ask this question, how do you differentiate between computer vision and machine vision?

 

Jeremy Bergh: [00:04:14] Yeah. Good question.

 

Winn Hardin: [00:04:14] Because that’s always been a question, right?

 

Jeremy Bergh: [00:04:16] Yeah, You know some gray area.

 

Jason Covar: [00:04:18] There’s definitely gray area. But I think when you when you think of machine vision, you know, you’re thinking more camera, light, controlling light, a library or software looking at a specific task. When you think of computer vision, I think it’s images that are taken in that may be more in a dynamic scenario. So, you know, when you talk about safety and some of these other applications that are within the space that are using components of machine vision today, I think those start to lend towards computer vision, where, you know, the conditions are not all perfect. You’re trying to to gather together multiple data points and improve processes. This is where the AI thing, right? That’s also a large —— large, vague subject matter. But when you start to look at it really at the ground level, a lot of that’s computer vision, I believe. And I think that’s where, yeah, we must be considering as we look forward.

 

Winn Hardin: [00:05:16] So does that actually include the embedded machine vision systems? And I know we’re going to talk about that a little bit later. Is that kind of a differentiator when we’re going for, you know, all-in-one-box systems versus separate lighting computer elements.

 

Jeremy Bergh: [00:05:27] Yeah, I would say so. I mean, I don’t know the pure definition, but, you know, it used to be a pretty clear cut line, I think, between computer vision and machine vision. And now you’re seeing, I think, more and more applications in machine vision where they’re trying to solve more interesting problems and automate things that aren’t always one specific way every time. So they’re bringing more of those machine learning algorithms into the embedded space and creating some of those. So now, does that cross over even though it’s in a machine vision manufacturing quality inspection environment? Is it now computer vision? I think there’s definitely some blurred lines.

 

Jimmy Carroll: [00:06:05] Yeah. And it’s almost like it doesn’t even matter, right? Like we’re using terms like AI and embedded vision and machine vision, and machine vision is probably the least nebulous of them all. But, like, embedded vision means a lot of different things to a lot of different people. AI means a lot of different things to a lot of different people. I remember not long ago I was looking to buy a small portable fan on Amazon for one of my kids, and it said on there like “AI smart chip.” And I’m like, well, no, it’s not. Like you’re just saying that.

 

Winn Hardin: [00:06:31] Cool sticker though.

 

Jeremy Bergh: [00:06:32] Yeah.

 

Jimmy Carroll: [00:06:33] And so, you know, just thinking about machine vision, it’s like, the technology has like … I’ve been saying this for a while, but it continues to veer out farther and farther from the factory floor. And whether or not you classify that as computer vision or machine vision, it’s a good thing either way that it’s being used in all these different ways.

 

Jeremy Bergh: [00:06:49] Good point.

 

Jason Covar: [00:06:50] And I think one area there is data collection, like trying to find a pattern or trying to analyze big data sets to solve something that’s not defined, right? I think that’s more in the lane of that. And to edge computing and embedded vision, which is, you know, sensors that would connect to that and you get closer to the problem. I think they’re closing the gap there, too, in terms of real world data versus other ways to actually solve something. So the technology is definitely a big part of that.

 

Winn Hardin: [00:07:28] No question.

 

Jimmy Carroll: [00:07:29] So, you guys deal a lot in embedded vision and when —— you touched on this a little bit —— and I imagine that as a result you’ve dealt with a lot of customers who are also incorporating AI. So I wanted to ask about, first, like what’s the latest and most interesting, from your perspective, in embedded vision? And how are you seeing AI complement that?

 

Winn Hardin: [00:07:49] Can I throw one more in there just to complicate things? How are manufacturers changing their attitudes towards their networks? Back in the day, if it wasn’t on premises, you weren’t going to do it. You weren’t going to see production off. AI has completely changed that. It’s in all the front offices, it’s in the cloud now. So is embedded vision and this changing attitude from manufacturers about loosening up the firewall a little bit?

 

Jason Covar: [00:08:10] Yeah, it’s a good question. We have, I believe, a unique perspective on this because, since 2019, we’ve been working with embedded vision hardware. So, MIPI CSI-2, FPD-Link cameras, GPUs… And so we introduced this technology, which was new. And we wanted to provide it in a kit form, thinking this would accelerate adoption. And so from the hardware perspective, we’ve been working for many years. We’ve also had opportunity to work in factories, you know, trying to get these innovative solutions into actual production. And you bring up a great point. One of the biggest roadblocks is IT, when you bring in these technologies. And it also is a big problem from adoption. So we’ve seen on-premises servers, we’ve seen cloud based systems —— particularly when you’re talking about putting things on assets to run. So IT is a very important part. You know, I say now if we were to take on a new opportunity, we must get check off from IT before we’d even consider an application, because it can live within a small project. But the scalability is not there without buy in from IT.

 

Winn Hardin: [00:09:20] Plus, maybe bringing them on board helps to reduce some of the barriers. I mean, if you can get IT as a champion on an internal project, you know you’re probably going to place that one on the floor.

 

Jason Covar: [00:09:29] Absolutely.

 

Jeremy Bergh: [00:09:30] And some places, you know, have their IT team doing all of their cybersecurity and some have separate teams. So you have to kind of see what the —— you know, take the temperature of the organization you’re working with and see who the stakeholders are. And make sure you start off on the right foot.

 

Jason Covar: [00:09:45] So I think what you’ll see is, starting to put these pieces together. From learning, there will be more of a solution that then can be offered to end users and have adoption from IT and customers. So I think these are still being developed, but I think this is what we’ll see in the next year or so.

 

Winn Hardin: [00:10:05] Is IT starting to come into the spec conversations and the initial, you know, site review and…

 

Jason Covar: [00:10:10] It must. Any innovative technology that you’re trying to introduce into the ecosystem, it has to. It has to be at the top of the list when you start any of these projects.

 

Winn Hardin: [00:10:24] So IT is not just going to say, “no, I only deal with OT. I’ll actually deal with your plant floor problems.”

 

Jason Covar: [00:10:31] And Jeremy can speak more to this on the cybersecurity side, but those that don’t talk about it early on, the meetings will be much, much different later on.

 

Winn Hardin: [00:10:40] Much less fun. There won’t be as many muffins.

 

Jimmy Carroll: [00:10:44] Yeah. If I could just go back to the beginning of that question a little bit. What are some of the fun applications that you guys have seen, or maybe some of the most notable or recurring applications when it comes to embedded vision that incorporates AI? I guess one of the questions I’m trying to get at is, like, everyone’s talking about AI. It was inevitable that we were going to talk to you about it. Where are you seeing it adding actual value?

 

Jeremy Bergh: [00:11:10] Yeah. I mean, everyone’s trying to experiment with how they can utilize it to make things more efficient and effective. Right? So there’s so many different areas that we’re playing around with now. Just conversations with customers from retail robotics to, you know, different quality inspection applications where they can maybe merge multiple different components at once instead of just having one line for each component. So you can get a little bit more creative in that regard. And running a classification algorithm, first to figure out is it widget A, B, or C? And then switching it to a different, you know —— It’s running, okay now, grade it. Is it an A, B, C, D part? So there’s different ways there. Retail robotics, you’ve seen different things. So it’s looking for presence, absence of items. Maybe running a price check on it to see if it’s actually a correct price. Whether or not the placement is there, I mean, there’s a little bit of everything.

 

Winn Hardin: [00:12:07] Absolutely.

 

Jason Covar: [00:12:09] And I also think what you’ll see in the future is it will be around the factory. More in monitoring, analyzing processes to be more efficient. And what I hear is that most factories in the future will have almost a digital twin. You’ll be taking real world analysis and comparing it to an optimized production. And to do that, to get that data set, yeah, you’ll have to use AI analytics, computer vision, in this case to collect that. And then you’ll be compared against basically an optimized factory. Yeah. And so I think we’re going…

 

Winn Hardin: [00:12:48] So let AI run the optimization, you know, of the workflow or whatever it is, rather than a person go, “okay. Try that. Okay. Try that. Okay. Try that.” A million iterations in a day or two.

 

Jason Covar: [00:12:59] That was a discussion towards the end of last year. And there was a representative from Nvidia there and she was asked, “what is the trend? What are you seeing with the enterprise level customers?” And that was her response. Everyone’s building out a digital twin and comparing it. And all the sensors that go inside of that are running different levels of AI to aggregate that data, to then do the comparison. So, again, going back to the first part of the conversation, I think for us in the, you know, component space that make the sensors that go into this, is something that we certainly have to be cognizant of.

 

Jimmy Carroll: [00:13:34] It’s almost a simplified way to look at it, but in a lot of ways, you think about what adding machine vision does to a robot system —— like a robot system by itself can be programmed to do certain, you know, fixed applications. But the parts have to be presented in a standard way. Adding vision adds an additional layer of flexibility, but then adding AI tools into that vision adds a little bit more flexibility. Like you’re saying, like being able to handle more part types or parts that are slightly out of position or organic or whatever, like that. And I know it’s kind of a simplified way to look at it, but that’s kind of the way that I’ve looked at it.

 

Jeremy Bergh: [00:14:09] That makes sense.Absolutely.

 

Winn Hardin: [00:14:11] Yeah. Or even a hybrid approach that’s still driving what we’re hearing the most of, right? Rules based programming for maybe defect identification.

 

Jeremy Bergh: [00:14:19] Yeah.

 

Winn Hardin: [00:14:19] But AI doing the grading, the early classification especially.

 

Jason Covar: [00:14:23] Right. And I think the tools have gotten much, much better. And the classical, you know, machine vision software that, you know, 5 to 10 years ago would make a specific task or decision, now has freedom to go without that one decision. And, yeah, it adds to the value of the system.

 

Jimmy Carroll: [00:14:40] And it’s also cool and, again, this is obvious to some extent, but it’s cool to see all these different technologies advancing sort of in lockstep to enable these new types of applications. You’re seeing a lot of this, too, and now with the availability of some of these application-specific, purpose-built systems that are all integrating robotics and AI and machine vision to do specialized tasks from whatever application type it is. But it’s all these technologies working together. You know, you mentioned simulation that, you know, computing and GPUs and FPGAs and things like that are helping AI get faster. Vision is helping. Anyway, I’m going down a road there…

 

Jeremy Bergh: [00:15:19] No, absolutely. We talk about it often. I mean, being in a machine vision camera company, sometimes, especially certain components can seem commoditized, right? There’re so many companies out there, you’re providing a component, and you don’t want it to be just a race to the bottom price-wise, right? You have to figure out how to bring value in some way. And so connecting and coordinating with the right people who are doing the software, doing other components, to be able to figure out, ‘how do we provide a solution that actually provides more value?’ is is where things are headed, I believe.

 

Jason Covar: [00:15:51] Yeah. And I’m a big proponent of ecosystem. I like companies that are built off of ecosystem, like organizations and industries that are built off ecosystem. I think ultimately everyone wins in that case. And it goes to your point, Jimmy, that, yeah, all of these things work together. And I think that’s the opportunity for us, like here at A3, is working with strategic partners and industry colleagues to make sure that we do work seamlessly together. And, yeah, ultimately I think it’s a win for everyone.

 

Winn Hardin: [00:16:22] And without getting into details —— because no one wants to talk about their financials, right? —— but the cameras on board, the board cameras, board-level cameras… Has the growth or the market growth at The Imaging Source been —— is it leaning towards more the embedded side? Is that seeing faster growth than traditional machine vision installations? Could you do both?

 

Jason Covar: [00:16:47] So yeah, we do both. For us, it’s a big part of our business. It’s something that we know well. And once you understand something, well, then you’re able to replicate that more, so.. On the one hand, yeah, absolutely, it’s a bigger growth. I think when you talk about the embedded vision side, I think it’s still there, I think the optimism is still ahead of us. It’s not fully matured.

 

Winn Hardin: [00:17:14] So we’re still selling more cameras and housings than we are board on chip.

 

Jason Covar: [00:17:17] And I think there’s a reason for that, because it’s —— you know, if you take a GigE camera, because of the work that’s been done for vision standards and so forth, it helps adoption [happen] much quicker. When you work with the component, and then you have a GPU system or a board, there’s a fragmentation there. Then you have a software layer, and then you’re helping define specs. So, it takes more time to get the adoption. But I think we’re getting closer and I think optimism is ahead of us.

 

Winn Hardin: [00:17:52] Right on.

 

Jason Covar: [00:17:52] Yeah.

 

Winn Hardin: [00:17:53] Well, it seems like consumer electronics has come a long way to help facilitate that. You know, in terms of the standards with data handling and integration of imaging systems and whatnot and cell phone, you know, UIs and APIs. That’s what you mentioned earlier in terms of,you guys have built your camera on board product line a little bit.

 

Jason Covar: [00:18:12] Correct. And I think it could get even more interesting now that the actual CMOS chips are getting more advanced. Because when you look at the design of a camera, right?, it simplifies things. But if you start to push more onto the actual CMOS and then you simplify back to actual processing, things start to get very interesting. And then if you put a specialized piece of software with it, it gets even more interesting.

 

Winn Hardin: [00:18:38] In terms of capabilities.

 

Jason Covar: [00:18:39] In terms of capabilities. Solving the problems, closer to the problem.

 

Jeremy Bergh: [00:18:43] Faster.

 

Jason Covar: [00:18:44] Faster, at a better cost and scalable because, yeah, you don’t need to outfit a line. You can basically deploy these things across the factory.

 

Winn Hardin: [00:18:54] So it seems critical in a world that’s going to be driven more by retrofits for a long time than it will be for greenfield builds, you know? We need to build a small form factor. You can put it anywhere, get that data, empower the ERP system, and then maybe we can start doing some real magical things about efficiency and optimization.

 

Jeremy Bergh: [00:19:12] Yeah, absolutely.

 

Winn Hardin: [00:19:13] Data collection is still the big roadblock there. You know, consistency.

 

Jimmy Carroll: [00:19:20] You guys are big in optical zoom cameras, as well. I wanted to touch on that and just see like, what’s the latest and greatest? What are you guys excited about? What are some applications?

 

Winn Hardin: [00:19:27] You guys are hitting on that?

 

Jeremy Bergh: [00:19:30] Yeah, we have one that we’re working on in logistics that I think is pretty interesting because you get so many different sizes of packages and everything going out. So, mounting a zoom and autofocus camera above an area where the boxes come by, you can make adjustments as they come through and capture the barcodes and everything that you want pretty quickly. So, and even add some software to do like OCR and have some inspection no matter what the orientation of the code or the writing is. So that’s one that came up recently that’s pretty interesting.

 

Jason Covar: [00:20:08] Yeah. I think it’s a convenience thing. What we found is, not for extremely high throughput applications, but if you’re outfitting a line with a traditional camera lens/lighting scenario, imagine you set up a line with 50 cameras and calibrate that and set all the apertures and so forth. Or if you can take a camera, mount it, and basically control it versus software after mounting. Because they also have autofocus and you’re able to adjust aperture. It’s a big cost savings in terms of setup as well as after you leave, right? So you deploy and then you can make adjustments remotely. I think the convenience, the single unit seems to be a value that people really like.

 

Winn Hardin: [00:20:55] Do you depend on a mechanical system? Back in the day, servo-based, auto zoom focus. It was just one more failure point of cameras. Right? And the throughput thing that you touched on, you know, when you’re in logistics or palletizing and everything, you’ve got plenty of time to acquire your image, resize your frame, do whatever you need to do. But have you taken some different approaches to the optical zooms to build in the reliability that the market wants?

 

Jason Covar: [00:21:17] I mean, I think it’s certainly not the camera for all applications, right? It’s certainly not that. It’s really a convenience for applications where you have the time. It’s not extremely high throughput and you’re looking for something with flexibility. I mean, that’s really where it is today.

 

Winn Hardin: [00:21:40] So, we’re here at the beginning of 2025. We’ve got some new fresh faces over the company. Is there anything particularly exciting for you guys this year? I know you can’t tell us about what’s coming out in the roadmap —— unless you want to! We’re listening. We’re all here.

 

Jeremy Bergh: [00:21:56] Yeah. We kind of touched on it a little bit before, but we’re always looking to figure out how we can get together some of the right players to create some more interesting solutions. And touch on more applications. So there’s definitely some ways we’re trying to take advantage of the technology, not just from what we create, but some of the ecosystem that we’re working with to come up with some interesting solutions. So hopefully you’ll see something soon from us in that regard.

 

Winn Hardin: [00:22:23] I look forward to that.

 

Jason Covar: [00:22:25] Indeed. Yeah. You know, as you innovate and you move forward, oftentimes you become more of a generalist and you step out of your lane, you try different things. And, we’re strong on being specialists, and getting together and “equal parts in” and then “equal parts out.” As Jeremy said, we’re looking forward to  this this year and, yeah, we’re very optimistic.

 

Jimmy Carroll: [00:22:47] What about any fun predictions you might have for the next couple of years that you expect to see…

 

Winn Hardin: [00:22:53] Or, like, who’s going to win the national championship tonight?

 

Jeremy Bergh: [00:23:00] I don’t have a dog in that hunt. I don’t want to piss off your audience.

 

Winn Hardin: [00:23:07] Especially in this environment.

 

Jimmy Carroll: [00:23:13] But seriously, in the overall industrial automation space: any predictions you expect in the next couple of years? They don’t have to be crazy. They don’t have to be correct.

 

Jeremy Bergh: [00:23:22] Good question. Do you have anything off the top of your head?

 

Jason Covar: [00:23:25] No, I just think we’re going to see more collaboration, you know, as the climate is as it is today. I think the community and the industry will get closer. I think you’ll see more collaborations. And I think that’s exciting.

 

Jimmy Carroll: [00:23:42] I want to see if you guys agree with this or not —— off the top of my head, I can’t remember if he said three years or five years, but I did a podcast with a super interesting guy recently that said, within 3–5 years, I expect that almost every robot cell in the world will have a camera in it. Does that seem plausible to you or…?

 

Jason Covar: [00:23:58] I think vision is big, right? When we talk about it within the vision portion of the association. Sometimes it gets overlooked. I would say that that’s certainly the case, but vision comes up often. And I think of it from a human perspective, like, what sensor would most people not want to lose in your sight?  People want to see things. And so, if it’s a robot or these other sensors, vision always is a critical piece. And so I don’t think that’s too far off.

 

Jeremy Bergh: [00:24:31] I think even if it’s not an area scan camera or a line scan camera, maybe some LiDAR or something, something to increase the capability of sensing whether or not it’s finding the distance or looking for specific parts. I think that makes sense.

 

Winn Hardin: [00:24:44] I know your ecosystem approach is certainly a smart way to tackle that problem too, right? So it’s critical whether you’re part of other ecosystems or you’re bringing others into yours.

 

Jason Covar: [00:24:52] And you see it across other businesses scaling up and getting there. Yeah. If you really join forces and you work together, you’re able to get there quicker.

 

Winn Hardin: [00:25:04] You know, it’s funny, we talk about trends all the time, but I kind of think that may be the most important. We talk about AI, deep learning, machine learning, all these things. Sure, they’re going to have a big impact, but traditionally manufacturers haven’t been the fastest to adopt new technologies. And then those suppliers or those automation suppliers haven’t always been… you’ve got some giants at the top. But there’s a lot of small and medium enterprises who are servicing the market. And of course they’re trying to protect their turf, right? Which slows down the growth of the ocean and lifting all boats. But that seems to really be changing a little bit.

 

Jeremy Bergh: [00:25:37] I agree.

 

Winn Hardin: [00:25:38] Very cool.

 

Jimmy Carroll: [00:25:40] Well guys, thank you so much. Really appreciate the time. Folks, if you want to learn more about what these guys are doing, it’s TheImagingSource.Com or we would be happy to field any questions at Manufacturing-Matters.com. And you can check out the rest of our episodes, including the very first podcast I ever did with Jeremy, which is a couple of years ago. It was a little bit shorter than this one. So thanks for circling back with me.

 

Jeremy Bergh: [00:26:00] My pleasure. We were standing at that time.

 

Jimmy Carroll: [00:26:02] We were. That was in Boston, right.

 

Jeremy Bergh: [00:26:04] In order for me to fit in the frame, I had to kind of duck down a little bit, right? Remember that?

 

Winn Hardin: [00:26:09] You had a wide stance, brother. We have pictures of that. Maybe we’ll send them over to Jason to get wider distribution at The Imaging Source.

 

Jimmy Carroll: [00:26:16] Well, again, thanks very much. We appreciate it.

 

Jason Covar: [00:26:18] Thank you very much for the opportunity.

 

Winn Hardin: [00:26:20] Our pleasure.

 

Jimmy Carroll: [00:26:20] Thanks, everybody, for tuning in.

 

Winn Hardin: [00:26:21] Til we see you again.