Outputs (2)

Autoencoders Without Reconstruction for Textural Anomaly Detection (2021)
Conference Proceeding
Adey, P., Akcay, S., Bordewich, M., & Breckon, T. (2021). Autoencoders Without Reconstruction for Textural Anomaly Detection. . https://doi.org/10.1109/ijcnn52387.2021.9533804

Automatic anomaly detection in natural textures is a key component within quality control for a range of high-speed, high-yield manufacturing industries that rely on camera-based visual inspection techniques. Targeting anomaly detection through the u... Read More about Autoencoders Without Reconstruction for Textural Anomaly Detection.

Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss (2021)
Conference Proceeding
Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2021). Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss. . https://doi.org/10.1109/icpr48806.2021.9413197

In this paper we introduce two methods of improving real-time object grasping performance from monocular colour images in an end-to-end CNN architecture. The first is the addition of an auxiliary task during model training (multi-task learning). Our... Read More about Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss.