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11.12.2016

Metal-sorting robot using hyperspecral imaging and perClass

Hyperspectral metal-sorting robot using perClass machine learningUniversity Liège has been using perClass to build a metal sorting system based on hyperspectral imaging. Classifying metals in hyperspectral images is very challenging task

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14.11.2016

From the Vision 2016 fair in Stuttgart

perClass machine learning course for data scientistsLast 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.

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14.11.2016

October 2016 course

perClass machine learning course for R&D specialistsIn 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.

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05.07.2016

Philips course in Eindhoven

perClass machine learning course for data scientistsIn the week 27 June to 1 July, we gave perClass training course in the High-Tech Campus Eindhoven. We enjoyed a great group of Philips professionals working on very diverse projects such as hyperspectral imaging, wearable electronics and medical devices.

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14.04.2016

Spring’16 perClass course

perClass Machine Learning course for R&D specialistsLast week, we had our Spring 2016 course. This time we had a very nice group of professionals working on defect detection, high-throughput agricultural quality control and traffic vision applications such as license plate recognition. As all the applications were based on imaging, we put more attention to extraction of image features (both at the local level of image neighborhoods and at the level of segmented-out objects). As usual in perClass courses, the participants took first steps on their own problems. We also discussed Deep Learning approach and delved into the basic building blocks of convolutional neural networks. (0) Comments

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