• Apricot 2 - CT imaging of whole fish and fillets 

      Bakken, Marianne; Schulerud, Helene (SINTEF Rapport;, Research report, 2016)
      The objectives of this project have been to image bones in whole fish and fillets in 9 different spices and to provide detailed information about the size, orientation and location of pinbones and the walking stick bone ...
    • Apricot anatomy. Measures of pinbones in fish fillets using CT 

      Bakken, Marianne; Schulerud, Helene; Thielemann, Jens T (SINTEF Rapport;, Research report, 2012)
      The objectives of this project have been to provide detailed information about the size, orientation and location of pinbones in fillets of cod, haddock, saithe and salmon. For each spices 16 fillets were CT scanned and ...
    • Automated 3D burr detection in cast manufacturing using sparse convolutional neural networks 

      Mohammed, Ahmed Kedir; Kvam, Johannes; Onstein, Ingrid Fjordheim; Bakken, Marianne; Schulerud, Helene (Peer reviewed; Journal article, 2022)
      For automating deburring of cast parts, this paper proposes a general method for estimating burr height using 3D vision sensor that is robust to missing data in the scans and sensor noise. Specifically, we present a novel ...
    • Autonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields 

      Ponnambalam, Vignesh Raja; Bakken, Marianne; Moore, Richard; Omholt Gjevestad, Jon Glenn; From, Pål Johan (Peer reviewed; Journal article, 2020)
      Automated robotic platforms are an important part of precision agriculture solutions for sustainable food production. Agri-robots require robust and accurate guidance systems in order to navigate between crops and to and ...
    • Bin Picking of Reflective Steel Parts Using a Dual-Resolution Convolutional Neural Network Trained in a Simulated Environment 

      Dyrstad, Jonatan Sjølund; Bakken, Marianne; Grøtli, Esten Ingar; Schulerud, Helene; Mathiassen, John Reidar Bartle (Chapter, 2019)
      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 ...
    • Bin Picking of Reflective Steel Parts Using a Dual-Resolution Convolutional Neural Network Trained in a Simulated Environment 

      Dyrstad, Jonatan Sjølund; Bakken, Marianne; Grøtli, Esten Ingar; Schulerud, Helene; Mathiassen, John Reidar Bartle (Chapter, 2019-12-15)
      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 ...
    • Bin Picking of Reflective Steel Parts using a Dual-Resolution Convolutional Neural Network Trained in a Simulated Environment 

      Dyrstad, Jonatan Sjølund; Bakken, Marianne; Grøtli, Esten Ingar; Schulerud, Helene; Mathiassen, John Reidar Bartle (Journal article; Peer reviewed, 2018-12-12)
      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 ...
    • Principal Feature Visualisation in Convolutional Neural Networks 

      Bakken, Marianne; Kvam, Johannes; Stepanov, Alexey; Berge, Asbjørn (Peer reviewed; Journal article, 2020)
      We introduce a new visualisation technique for CNNs called Principal Feature Visualisation (PFV). It uses a single forward pass of the original network to map principal features from the final convolutional layer to the ...
    • Robot-supervised Learning of Crop Row Segmentation 

      Bakken, Marianne; Ponnambalam, Vignesh Raja; Moore, Richard J.D.; Omholt Gjevestad, Jon Glenn; From, Pål Johan (Chapter; Peer reviewed, 2021)
      We propose an approach for robot-supervised learning that automates label generation for semantic segmentation with Convolutional Neural Networks (CNNs) for crop row detection in a field. Using a training robot equipped ...