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John Reidar Mathiassen

Senior Research Scientist

John Reidar Mathiassen

Senior Research Scientist

John Reidar Mathiassen
Phone: 934 53 696
Department: Seafood Technology
Office: Trondheim

Publications and responsibilities

Publication
https://www.sintef.no/en/publications/publication/?pubid=CRIStin+1724454

The robotic handling of compliant and deformable food raw materials, characterized by high biological variation, complex geometrical 3D shapes, and mechanical structures and texture, is currently in huge demand in the ocean space, agricultural, and food industries. Many tasks in these industries are...

Authors Misimi Ekrem Olofsson Alexander Eilertsen Aleksander Øye Elling Ruud Mathiassen John Reidar Bartle
Year 2018
Type Conference lecture and academic presentation
Publication
https://www.sintef.no/en/publications/publication/?pubid=CRIStin+1687425

We consider the case of robotic bin picking of reflective steel parts, using a structured light 3D camera as a depth imaging device. In this paper, we present a new method for bin picking, based on a dual-resolution convolutional neural network trained entirely in a simulated environment. The dual-r...

Publication
https://www.sintef.no/en/publications/publication/?pubid=CRIStin+1665083

We consider the case of robotic bin picking of reflective steel parts, using a structured light 3D camera as a depth imaging device. In this paper, we present a new method for bin picking, based on a dual-resolution convolutional neural network trained entirely in a simulated environment. The dualre...

Year 2018
Type Conference lecture and academic presentation
Publication
https://www.sintef.no/en/publications/publication/?pubid=CRIStin+1611909

We teach a real robot to grasp real fish, by training a virtual robot exclusively in virtual reality. Our approach implements robot imitation learning from a human supervisor in virtual reality. A deep 3D convolutional neural network computes grasps from a 3D occupancy grid obtained from depth imagi...

Year 2018
Type Journal publication
Publication
https://www.sintef.no/en/publications/publication/?pubid=CRIStin+1572396

We present an approach to robotic deep learning from demonstration in virtual reality, which combines a deep 3D convolutional neural network, for grasp detection from 3D point clouds, with domain randomization to generate a large training data set. The use of virtual reality (VR) enables robot learn...

Year 2018
Type Journal publication
Publication
https://www.sintef.no/en/publications/publication/?pubid=CRIStin+1561519

Despite advances in computer vision and segmentation techniques, the segmentation of food defects such as blood spots, exhibiting a high degree of randomness and biological variation in size and coloration degree, has proven to be extremely challenging and it is not successfully resolved. Therefore,...

Year 2017
Type Journal publication