Marianne Bakken
Research Scientist/PhD Fellow
Marianne Bakken
Research Scientist/PhD Fellow
Publications and responsibilities
Autonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields
Fast reasoning visualization for deep convolutional networks
Principal Feature Visualisation in Convolutional Neural Networks
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 nal convolutional layer to the original image space as RGB channels. By working on a batch of images we can...
End-to-end learning for autonomous crop row-following
End-to-end Learning for Autonomous Navigation for Agricultural Robots
Bin Picking of Reflective Steel Parts Using a Dual-Resolution Convolutional Neural Network Trained in a Simulated Environment
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...
End-to-end Learning for Autonomous Navigation for Agricultural Robots
Bin Picking of Reflective Steel Parts using a Dual-Resolution Convolutional Neural Network Trained in a Simulated Environment
Apricot 2 - CT imaging of whole fish and fillets
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 in fillets. For each spices 2-4 fillets were CT scanned and analyzed. The bones...