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June 26, 2017

perClass integrated in a real-time spectral imaging systems

We’re happy to announce a partnership with Cubert GmbH a pioneer in real-time spectral imaging. Via this partnership, perClass comes tightly integrated into Cubert software and directly useful for all Cubert customers. I comes as a part of standard Cubert Utilities installer.

Users may easily import acquired hyperspectral images to perClass, build custom classifiers and export them directly as Cubert XML plugins. Classifiers can then be loaded into Cubert Touch environment processing live or pre-recorded imagery.

Joint press-release:

PR Sys Design and Cubert GmbH are happy to announce their partnership bringing advanced machine learning technology to Cubert ecosystem.

perClass software package, developed by PR Sys Design, enables researchers and industrial R&D specialists to create powerful classification solutions for a complete range of Cubert real-time spectral cameras.

Main contribution of this partnership is that Cubert users may, within five minutes, record spectral images, create statistical classifiers for specific materials and deploy such solution processing a live data stream.

This enables the users to identify types of plastics, sort natural products such as vegetable or nuts, classify types of tissues and biological material, analyze tree types and plant health in remote sensing images and more.

perClass comes tightly integrated within the 2.0.4 “Fuchsia” release of Cubert Utilities. Users may install perClass Toolbox for Matlab(R) directly via the Cubert installer. Multi- and hyperspectral images, acquired by the complete range of Cubert cameras, may be directly imported into perClass environment. A rich set of interactive visualization, data modeling and classification tools are available for rapid prototyping.

Unique concept of perClass pipelines allows users to quickly model and test diverse data processing chains. This includes practical setups such as one-class detectors, multi-class discriminants and cascaded systems. Classification algorithms can process entire spectrum or only specific, automatically selected, spectral bands. Solution performance can be fine-tuned based on application-specific requirements, mitigating specific types of errors.

The major improvement over other machine learning toolkits is the direct deployment of any data processing pipeline outside perClass environment. Classification models can be directly exported as Cubert plugins and applied to pre-recorded or live data in Cubert Touch application.

perClass integration into Cubert ecosystem significantly lowers time to market for a new generation of intelligent products based on advanced machine learning technology.

perClass - Cubert partnership