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Michael Gibbons

How did you come to be part of the imaging/machine vision industry?

I worked in the technology industry on the business side for several years before going back to school to study embedded systems development. My first experience in the machine vision industry was in 2002 when I joined Point Grey as their first technical support engineer. At that time the company was much smaller – we were only 18 people – so there were many opportunities for me to learn and grow. I started by providing technical and applications engineering support to our customers worldwide, which gave me the chance to deeply understand Point Grey’s products, the needs of our customers, and the challenges they face. I began to expand the scope of my role as the company and our customer base grew, taking over all of our technical writing and camera testing, and later on expanding into product management and marketing. The teams I was leading in these areas steadily grew, and then – about four years ago – I had the opportunity also to take on the management of sales and business development. It’s been an enjoyable ride and I’ve been fortunate to work with an amazing group of people. With more than 250 employees today, Point Grey has grown to be a market leader and I’ve had the privilege of growing with them.

What role does North America have in the development of machine vision?

North America plays an important role for many reasons. There’s a very broad and unique spectrum of industries represented in North America – everything from industrial and factory automation to healthcare, medical and scientific. And within each of these industries are verticals, like 3D scanning or electronics inspection, each of which has its own unique challenges when it comes to vision. What we find fascinating is watching how advances in vision in one vertical often translate, or ‘cross-pollinate’, advances in another.

There is also a very big academic base dedicated to vision research here in North America, and we find many of the most innovative new techniques come out of the work done at universities and research institutes. We often hear of big OEM customers collaborating with local schools to draw on their creativity and help them look at complex problems in new ways.

Some of the biggest technology companies in the world are also based in the US, and many of those companies are critical for vision. For example, Intel is one of the main providers for USB 3.0 connectivity with their chipsets. The work that they do on ensuring high performance, quality, and reliability of their products has a direct impact on the experience of our USB 3.0 customers. Google is investing in self-driving cars and Facebook is investing in immersive virtual reality experiences, and the vision work that they’re doing as part of these initiatives is truly ground-breaking. Apple continues to produce more devices with increasing complexity, which continues to drive higher and higher requirements for more complex vision systems. And North America serves as home base for a number of industry associations, including the AIA, RIA, and A3, that are key to the success of machine vision.

What do you see as the major growth sectors?

We’re seeing most of the growth coming outside of the factory floor. Specifically we’re seeing major growth in 3D scanning, healthcare, medical, and biometrics. We’re also seeing growth in relatively new markets such as retail. 3D stereo vision is becoming a more predominant technology for use in people counting and tracking in brick and mortar stores and airports. We also feel that advances in camera technology, including higher speed and more sensitive image sensors, smaller and more lightweight electronics, and lower costs will lead to faster, smaller, and more accurate 3D scanning systems.

What are the important technological challenges facing the industry?

I think one challenge is the increasing complexity of machine vision applications. Virtually every machine vision software provider has algorithms for standard machine vision tasks. Where scanning and reading a barcode might have been a challenge several years ago, now it’s a fairly straightforward task. But how do you properly inspect a cherry? What differentiates a good cherry from a bad one is not so straightforward. The size, the shape, presence of defects, and the colour are just some of the factors that need to be evaluated. Self-driving cars are another great example of the challenges faced by machine vision designers today. Navigating along a straight stretch of pristine highway is one thing; making a decision when faced with a cat crossing the road in a construction zone on a rainy day is a different thing entirely. And in both of these examples, the challenges aren’t limited to software, but also to hardware. Getting cameras that are small enough, with enough performance, and at low cost, is pushing companies like Point Grey to continually innovate.

What will be the most significant commercial change in the industry in the coming years?

The line between consumer and industrial technology companies is getting fuzzier. You can use your smartphone to take a panoramic image or get great images from a GoPro. There’s a lot of good image sensor technology that can be put into a smartphone, or a webcam or into an iPad at a very little cost. Naturally, these potentially threaten machine vision.

Industry consolidation is another big commercial change that we’re seeing and will continue to see. The hope continues to be that it will drive innovation, particularly with the image sensor vendors, because there will be more competition between sensor manufacturers such as Sony, Cmosis, On Semiconductor, and Sharp, among others, which will drive them to create amazing new technology.


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