Tell us a bit about Immervision, of which you are CEO
Immervision is a world leader in 360° panoramic imaging founded in France in 2000. Located in Montreal since 2003, Immervision today patents its panomorphic optical technology and image-processing algorithms and licenses them to lens producers, product manufacturers and software developers around the world.
After collaborating with more than 35 security camera brands and participating in the development of over 100 video-surveillance software products worldwide, Immervision entered the mobility field in 2017. We are currently working with several industry players to increase the perception and accuracy of vehicle vision systems, such as LeddarTech, a global leader in LiDAR technologies. It's producing something very interesting! In fact, there is a major global announcement coming… Stay tuned!
Immervision was born in France. Why did you choose Quebec for its development?
At the time, in the early 2000s, France was too far from our market, which was mainly in North America and Asia. Investissement Québec attracted us with tax credits for research and development. Their excellent outreach work and help integrating companies were decisive for our transition.
What is panoramic vision? Can you give a concrete example of what it can do, daily, for the average person?
It's a subject for debate, but connected devices, increasingly popular in the home, all have miniature cameras that capture and analyze our movements. Surveillance cameras, for homes or public buildings, also use panoramic vision.
Wide-angle lenses are also used on car cameras. Several cellphone brands, like Motorola, have also adopted 360° capture, not to mention GoPro cameras, which are increasingly popular worldwide.
What is the optics-photonics industry, and what is its importance for the development of intelligent mobility?
It's intelligent robotic vision that allows data collected by wide-angle cameras to be presented in an intelligible way for artificial intelligence algorithms. Intelligent mobility depends 100% on understanding the environment. It doesn't just require vision, but also—and above all—understanding of what is seen. Therefore the three aspects of autonomy must be present: perception, stemming from very high-quality optics; data analysis, including information aggregation; and finally, action.
What challenges remain to be addressed in the coming years?
We are working on better image capture, but the challenge remains to deliver the information to artificial intelligence algorithms in real time, in order to obtain a better understanding of what is seen. We are working on ways to simplify these processes so there is no delay. That's the key in transport autonomy. It's what we call the "Data in Picture."
What's it like to be a supplier to large global transportation companies?
We are much smaller than the giants we work with! But they all have a great need for smaller players, like us, to advance their technologies. For these big companies, we are therefore instigators of innovation. We help them think about technologies they hadn't necessarily considered. It's work in which we are involved very early in the process.
The ecosystems of these big players are complex. Being smaller also allows us to be highly responsive. You must also not be afraid to invest! It's the key to our credibility.
How do you experience being a woman in the male-dominated world of transportation?
I do not deny that there are problems, far from it. But personally I have never encountered an obstacle as a woman with regard to the commercialization of our technologies. I have never felt that my gender was a handicap. I have never considered it to be one either.
In the world of finance, however, you have to work very hard as a woman to be taken seriously. It's a whole different world. I have often felt it, for example when seeking financing.
In your opinion, what will be the greatest advance in the field of wide-angle imaging in the coming years?
It will be to bridge it with artificial intelligence. Today's vision is based on narrow-angle vision. We must work to ensure that vision becomes perception, that is, that it also provides context to images and to algorithms.
The idea is to reproduce the human brain. The more intelligible sensor data is to artificial intelligence algorithms, the smarter mobility will be.
