Peter H. N. de With — accepted AI-conference papers

2024

Retaining Informative Latent Variables in Probabilistic Segmentation

M. M. Amaan Valiuddin, Christiaan G. A. Viviers, Ruud van Sloun, Peter H. N. de With, Fons van der Sommen

ICASSP 2024accepted

Conditional latent-variable models can successfully quantify annotation variability in segmentation. Training such models involves tuning the dimensionality of the latent space to optimally capture the inherent data ambiguity. Nevertheless, we discover after careful tuning, that the latent space doe…

Cited by 0 Source ↗Scholar ↗

2024

Supervised Representation Learning Towards Generalizable Assembly State Recognition

Tim J. Schoonbeek, Goutham Balachandran, Hans Onvlee, Tim Houben, Shao-Hsuan Hung, Jacek Kustra +2

RA-L 2024

Assembly state recognition facilitates the execution of assembly procedures, offering feedback to enhance efficiency and minimize errors. However, recognizing assembly states poses challenges in scalability, since parts are frequently updated, and the robustness to execution errors remains underexpl

Cited by 3 Source ↗Scholar ↗

2016

Hierarchical 2.5-D Scene Alignment for Change Detection With Large Viewpoint Differences

Dennis W. J. M. van de Wouw, Gijs Dubbelman, Peter H. N. de With

RA-L 2016

Change detection from mobile platforms is a relevant topic in the field of intelligent vehicles and has many applications, such as countering improvised explosive devices (C-IED). Existing real-time C-IED systems are not robust against large viewpoint differences, which are unavoidable under realist

Cited by 11 Source ↗Scholar ↗