Rick Groenendijk

Rick Groenendijk is a researcher in the field of computer vision and mathematical morphology at the University of Amsterdam's Computer Vision Lab. His work focuses on integrating morphological operators into deep learning frameworks, particularly in neural network architectures. He holds a PhD in Computer Vision & Mathematical Morphology from the University of Amsterdam, supervised by Dr. Leo Dorst and Prof. Theo Gevers, which he successfully defended in October 2023.

His research interests include advancing hybrid neural network architectures, such as HaarNet and MorphPool, which combine linear and morphological operations for tasks like semantic segmentation and depth estimation. Recent contributions address challenges in RGB-D data processing and adversarial training. He has also explored applications in photogrammetry and social robotics learning.

Publications span top venues including IEEE TPAMI, arXiv, and the British Machine Vision Conference. His code repository for the Geometric Back-Propagation paper is publicly available on GitHub under the MIT license.

Rick has collaborated with institutions on multi-loss optimization techniques and climate action analysis through machine learning. While no formal awards are listed, his work demonstrates impactful contributions to computer vision and deep learning methodologies.