collaborators

5 papers

cs.CV2026

End-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines

Raül Pérez-Gonzalo, Andreas Espersen, Søren Forchhammer +1

Transferring large volumes of high-resolution images during wind turbine inspections introduces a bottleneck in assessing and detecting severe defects. Efficient coding must preser…

cs.CV2026

Synthetic Craquelure Generation for Unsupervised Painting Restoration

Jana Cuch-Guillén, Antonio Agudo, Raül Pérez-Gonzalo

Cultural heritage preservation increasingly demands non-invasive digital methods for painting restoration, yet identifying and restoring fine craquelure patterns from complex brush…

cs.CV2026

Probabilistic Deep Discriminant Analysis for Wind Blade Segmentation

Raül Pérez-Gonzalo, Andreas Espersen, Antonio Agudo

Linear discriminant analysis improves class separability but struggles with non-linearly separable data. To overcome this, we introduce Deep Discriminant Analysis (DDA), which dire…

cs.CV2026

Discriminant Learning-based Colorspace for Blade Segmentation

Raül Pérez-Gonzalo, Andreas Espersen, Antonio Agudo

Suboptimal color representation often hinders accurate image segmentation, yet many modern algorithms neglect this critical preprocessing step. This work presents a novel multidime…

cs.CV2026

Unsupervised Modular Adaptive Region Growing and RegionMix Classification for Wind Turbine Segmentation

Raül Pérez-Gonzalo, Riccardo Magro, Andreas Espersen +1

Reliable operation of wind turbines requires frequent inspections, as even minor surface damages can degrade aerodynamic performance, reduce energy output, and accelerate blade wea…