1 citations · 1 across the 6 of their papers we have counts for
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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…
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…
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…