1 citations · 1 across the 7 of their papers we have counts for
8 papers
Deep Image Segmentation via Discriminant Feature Learning
Adam Dawid Sztamborski, Raül Pérez-Gonzalo, Antonio Agudo
Accurate image segmentation remains challenging, particularly in generating sharp, confident boundaries. While modern architectures have advanced the field, many of them still rely…
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…
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…
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…
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…
Dual-Space Augmented Intrinsic-LoRA for Wind Turbine Segmentation
Shubh Singhal, Raül Pérez-Gonzalo, Andreas Espersen +1
Accurate segmentation of wind turbine blade (WTB) images is critical for effective assessments, as it directly influences the performance of automated damage detection systems. Des…