About This Webinar
Machine vision is increasingly important for many applications, such as object classification. However, relying on conventional RGB imaging is sometimes insufficient. The input images are just too similar, regardless of algorithmic sophistication.
Classifying visually indistinguishable objects is highly desirable for many applications: sorting for recycling, identifying unwanted foreign objects, assessing food ripeness, monitoring crop growth, performing geological surveillance, and more. This capability creates an extensive opportunity for hyperspectral imaging, which adds the extra dimension of wavelength to conventional images.
Thus far, its the technology’s adoption has largely been restricted to relatively niche applications. With further improvements in cost, data analysis capabilities, and business models that better meet customer needs, there is extensive scope for hyperspectral imaging to be much more widely adopted across markets as varied as autonomous vehicles and cosmetics.
Dyson outlines hyperspectral imaging basics, along with potential future development directions and emerging applications. In particular, he discusses the opportunities for emerging cheaper SWIR imaging technologies within the hyperspectral imaging space. He also covers how hyperspectral imaging competes with other technologies, along with the size and scope of the potential markets.
***This presentation premiered during the 2022
Vision Spectra Conference. For more information on Photonics Media conferences, visit
events.photonics.com.
About the presenter:
Matthew Dyson is a senior technology analyst at IDTechEx, based in London. He received master’s and doctorate degrees in physics from Imperial College London, where he investigated the optoelectronic properties of organic semiconductors. This was followed by post-doctoral research at Eindhoven Technical University in the Netherlands, where he focused primarily on organic photodetectors. At IDTechEx, Matthew utilizes this technical background to cover both emerging image sensor technologies and printed/flexible electronics.