Development of the LISA framework for illumination-invariant hyperspectral imaging analysis in precision viticulture. An end-to-end IoT-enabled robotic system for non-destructive, real-time mapping of grape yield and quality.
@article{cornelissen2025vineyard,title={In-Field Mapping of Grape Yield and Quality with Illumination-Invariant Deep Learning},author={Cornelissen, Ciem and De Coninck, Sander and Willekens, Axel and Leroux, Sam and Simoens, Pieter},journal={IEEE Internet of Things Journal},year={2025},}
WACV
Adaptive Clustering for Efficient Phenotype Segmentation of UAV Hyperspectral Data
Ciem Cornelissen, Sam Leroux, and Pieter Simoens
In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025
Introduction of the OHSLIC algorithm for computationally efficient, real-time phenotype segmentation from hyperspectral imaging data. Enables on-device processing on UAVs.
@inproceedings{cornelissen2025ohslic,title={Adaptive Clustering for Efficient Phenotype Segmentation of UAV Hyperspectral Data},author={Cornelissen, Ciem and Leroux, Sam and Simoens, Pieter},booktitle={IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops},year={2025},}
NCA
Computational Fairness in Adaptive Neural Networks
Sam Leroux, Ciem Cornelissen, Vishisht Sharma, and 1 more author
A novel investigation into the fairness of adaptive neural networks, analyzing disparities in computational resource allocation across demographic subgroups. Introduces computational resource allocation as a new dimension of AI fairness.
@article{cornelissen2025fairness,title={Computational Fairness in Adaptive Neural Networks},author={Leroux, Sam and Cornelissen, Ciem and Sharma, Vishisht and Simoens, Pieter},journal={Neural Computing and Applications},year={2025},}
Master’s thesis in Artificial Intelligence exploring the application of quantum computing techniques to Earth observation and remote sensing challenges.
@mastersthesis{cornelissen2023quantum,title={Quantum Computing for Earth Observation},author={Cornelissen, Ciem},year={2023},school={KU Leuven},}
2022
Criticality and Forecasting of the Cryptocurrency Market
Master’s thesis in Physics applying advanced computational and statistical methods to model complex, non-linear, and stochastic systems in financial markets.
@mastersthesis{cornelissen2022crypto,title={Criticality and Forecasting of the Cryptocurrency Market},author={Cornelissen, Ciem},year={2022},school={Ghent University},}
