Publications

Recently, transformer networks have outperformed traditional deep neural networks in natural language processing and show a large potential in many computer vision tasks compared to convolutional backbones. In the original transformer, readout tokens are used as designated vectors for aggregating information from other tokens. However, the performance of using readout tokens in a vision transformer is limited. Therefore, we propose a novel ...

Training a single model jointly for detection and recognition is typically done with a dataset that is fully annotated, i.e., the annotations consist of boxes with class labels. In the case of retail product detection and recognition, however, developing such a dataset is very expensive due to the large variety of products. It would be much more cost-efficient and scalable if we could employ two task-specific datasets: one detection-only and one ...

To guarantee integrity when trading diamonds, a certified company can grade the diamonds and give them a unique ID. While this is often done for high-valued diamonds, it is economically less interesting to do this for lower-valued diamonds. While integrity could be checked manually as well, this involves a high labour cost. Instead, we present a computer vision-based technique for diamond identification. We propose to apply a polar ...

With the ever increasing need for better cybersecurity, and due to the continuous growth of network traffic bandwidths, there is a continuous pursuit of faster and smarter network intrusion detection systems. Neural network-based solutions on FPGAs are very effective in detecting different types of attacks, but have problems with analyzing network traffic online at line speed. One important bottleneck that limits the throughput in raw ...

Recently, several works have proposed fusing radar data as an additional perceptual signal into monocular depth estimation models because radar data is robust against varying light and weather conditions. Although improved performances were reported in prior works, it is still hard to tell how much depth information radar can contribute to a depth estimation model. In this paper, we propose radar inference and supervision experiments to ...

Integrating multi-sensor systems to sort and monitor complex waste streams is one of the most recent innovations in the recycling industry. The complementary strengths of Laser-Induced Breakdown Spectroscopy (LIBS) and computer vision systems offer a novel multi-sensor solution for the complex task of sorting aluminum (Al) post-consumer scrap into alloy groups. This study presents two novel methods for fusing RGB and Depth images with LIBS using ...

Historical land cover (LC) maps are an essential instrument for studying long-term spatio-temporal changes of the landscape. However, manual labelling on low-quality monochromatic historical orthophotos for semantic segmentation (pixel-level classification) is particularly challenging and time consuming. Therefore, this paper proposes a methodology for the automated extraction of very-high-resolution (VHR) multi-class LC maps from historical ...