Simplifying deployment of hyperspectral imaging

03 February 2020

Pleora Technologies and perClass BV have announced a technology partnership to simplify the deployment of machine learning hyperspectral imaging for inspection applications.

Where traditional cameras capture images in broad red, green, and blue wavelengths to match human vision, hyperspectral imaging provides narrower wavelengths to include ultraviolet or near infrared information for each pixel on an inspected object. 

With the ability to capture the entire electromagnetic spectrum, hyperspectral imaging is being increasingly adopted across multiple industries to analyse, detect, and classify materials.
 
Food inspection, for example, is adopting hyperspectral imaging to detect foreign materials and ensure products meet quality standards while reducing costly visual inspection. In the pharmaceutical market, hyperspectral imaging can detect subtle changes in the composition of active ingredients in visually identical pills to screen out-of-specification products.  
 
“Hyperspectral imaging provides deeper insight across a widening range of markets, but end-users and integrators consistently struggle with deployment,” said Jonathan Hou, chief technology officer at Pleora. “By including powerful automatic machine learning capabilities from perClass as a plug-in solution in our AI Gateway, Pleora is delivering the vision industry’s most straightforward solution to train and deploy AI algorithms leveraging hyperspectral imaging for inspection applications.” 
 
With the Pleora AI Gateway and perClass AI plug-in, end-users and integrators can deploy machine learning hyperspectral capabilities without any additional programming knowledge. Images and data are uploaded to perClass Mira ‘no code’ training software on a host PC, which automatically generates AI models that are deployed on the Pleora AI Gateway in a production environment.
 
Pleora’s AI Gateway works seamlessly with any standards-compliant hyperspectral sensor. Many software processing solutions require custom workarounds to support hyperspectral through GigE Vision because they cannot interpret multiband information. In comparison, the AI Gateway bridges the gap between applications and existing machine vision software by automatically handling image acquisition from the hyperspectral imaging source and sending out the processed data over GigE Vision to inspection and analysis platforms.


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