Last week, we gave the 19th edition of Machine Learning course for practitioners. We have discussed a broad range of applications including defect detection, geophysical applications, medical support systems and remote sensing.
What a busy week! Another event related to food processing, this time in Brightlands Venlo. Live demo of spectral image interpretation gave us a unique opportunity to understand challenges of practitioners from food production in this part of The Netherlands.
We gave a live demo at specialized event on Photonics for Agro&Food indutry.
perClass sponsored SpectroExpo event, part of WHISPERS conference on hyperspectral imaging in Amsterdam. It was a great opportunity to meet both our industrial partners and users of spectral imaging systems.
Happy to announce that SpectraPartners became a distributor of perClass software in Benelux!
Users of Imec spectral cameras may now benefit from a tight integration between perClass Mira and HSIViewer.
New release of perClass Mira brings many enhancements such as confusion matrix visualization of error structure, interactive performance fine-tuning, and RGB preview mode.
The research team of RECENDT has published prrogress on their new mid-infrared hypespectral scanner illustrating material separation using perClass Mira
Happy to share a publication made by Wageningen WUR researchers who used perClass Mira together with Specim I camera to validate authenticity of bananas.
Together with SpectraPartners, distributor of Specim spectral cameras, we have given a joint demo at Wageningen WUR Summerschool of plant phenotyping. Participants could see spectral image acquisition and interpretation in minutes without programming.
The video of our presentation at Chii 2018 hyperspectral workshop in Graaz is now available.
We are happy to announce our Machine Learning autumn course. You are welcome to bring your own data for the practicum. The course includes hands-on exercises on Deep Learning. Would you like to join? Register now.
Creating classification solutions for hyperspectral images is now only a matter of few minutes. In this short video Cubert illustrates the workflow for spices classification. In perClass Mira the user can easily create a classification solution. Cubert has integrated perClass Runtime in their camera acquisition software. In this way the classification model can be applied directly to the live camera feed.
We have participated in the Chii 2018 hyperspectral imaging workshop in Graz, Austria. Happy to share huge amount of interest in our perClass Mira user interface.
perClass was represented at the Vision & Robotics trade-fair in Veldhoven, The Netherlands at the stand of SpectraPartners.
We have welcome a diverse group of researchers working on applications in industrial quality control, agro & food robotics, traffic and maintenance, brain computer interfaces and multimedia content analysis.
At Agri&Food fair, we have demonstrated the first public beta of the perClass Mira environment for user-friendly interpretation of spectral images.
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.
In the last week course, we had welcomed participants from Netherlands, Belgium, UK and Spain.
University Liège has been using perClass to build a metal sorting system based on hyperspectral imaging. Classifying metals in hyperspectral images is very challenging task
Last week, we participated in the Vision Trade Fair in Stuttgart, Germany, the world’s leading machine vision event. It was our fourth exhibition and, by far, the most exciting. While few years back, we had to explain what we mean by Machine Learning, today’s environment and public perception entirely changed.
In our October course, we have welcomed a group of 10 researchers from The Netherlands, Mexico and Germany. Participants brought in a lot of enthusiasm and interest especially in Deep Learning.