Santiago Velasco-Forero

Researcher Scientist

Center · STIM

Keywords

Awards & distinctions

2016 Prix de “Outstanding reviewer: pour Pattern Recognition. 1 février 2016

Team

STIM

Biography

Santiago Velasco-Forero is a researcher whose work lies at the intersection of mathematical morphology, deep learning, and the processing of spatial and multidimensional data. His research focuses primarily on integrating morphological operators into neural architectures, with the aim of combining the robustness of nonlinear methods with the learning capabilities of neural networks. In particular, he explores theoretical approaches to generalize morphological operators to equivariant frameworks, such as group transformations, while developing practical applications in medical imaging, 3D point cloud analysis, and radar signal processing. His expertise also includes the design of interpretable models, leveraging concepts such as geodesic reconstructions and differential invariants, for tasks such as segmentation, classification, and anomaly detection. His recent contributions address methodological challenges, such as the optimization of morphological networks or the evaluation of generative data, while proposing solutions tailored to specific industrial or scientific contexts.

Publication(s)

2026
Learning Morphological Representations of Image Transformations: Influence of Initialization and Layer Differentiability DOI : 10.1007/978-3-032-09544-2_27
2026
Approximating Condorcet Ordering for Vector-Valued Mathematical Morphology DOI : 10.1007/978-3-032-09544-2_32
2025
Cell Counting with Trainable h-Maxima and Connected Component Layers DOI : 10.1007/s10851-025-01243-z
2025
Deep learning approach for airborne alpha radioactivity monitoring in atypical atmospheric conditions DOI : 10.1016/j.jaerosci.2025.106573
2025
Group Equivariant Morphological Networks DOI : 10.1137/24M1685766
2025
MorphoSkel3D: Morphological Skeletonization of 3D Point Clouds for Informed Sampling in Object Classification and Retrieval DOI : 10.1109/3DV66043.2025.00128
2025
MFN Decomposition and Related Metrics for High-Resolution Range Profiles Generative Models DOI : 10.1109/RadarConf2559087.2025.11204884
2024
A POSTERIORI DEEP LEARNING SEGMENTATION QUALITY ESTIMATION BASED ON PREDICTION ENTROPY DOI : 10.5566/ias.3024
2024
Choquet Capacity Networks for Random Point Process Classification and Regression DOI : 10.1007/978-3-031-58665-1_18
2024
Spatial Reasoning Loss for Weakly Supervised Segmentation of Skin Histological Images DOI : 10.1109/ISBI56570.2024.10635595
2024
Automatic yarn path extraction of large 3D interlock woven fabrics with confidence estimation DOI : 10.1016/j.compositesa.2024.108396
2024
Deep Matrix Profile for Maneuver Classification in Low Earth Orbit Satellite Trajectories DOI : 10.23919/eusipco63174.2024.10715206
2024
Counting Melanocytes with Trainable h-Maxima and Connected Component Layers DOI : 10.1007/978-3-031-57793-2_32
2024
Group Equivariant Networks Using Morphological Operators DOI : 10.1007/978-3-031-57793-2_13
2023
Real-time Classification of Aircrafts Manoeuvers DOI : 10.1007/s11265-022-01823-x
2023
Rotation-invariant Hierarchical Segmentation on Poincaré Ball for 3D Point Cloud DOI : 10.1109/ICCVW60793.2023.00192
2023
Near Out-of-Distribution Detection for Low-Resolution Radar Micro-doppler Signatures DOI : 10.1007/978-3-031-26412-2_24
2023
Moving Frame Net: SE(3)-Equivariant Network for Volumes
2023
Can Generalised Divergences Help for Invariant Neural Networks? DOI : 10.1007/978-3-031-38271-0_9
2023
GenHarris-ResNet: A Rotation Invariant Neural Network Based on Elementary Symmetric Polynomials DOI : 10.1007/978-3-031-31975-4_12
2022
FULLY TRAINABLE GAUSSIAN DERIVATIVE CONVOLUTIONAL LAYER DOI : 10.1109/ICIP46576.2022.9897734
2022
Scale-Equivariant U-Net
2022
Irregularity Index for Vector-Valued Morphological Operators DOI : 10.1007/s10851-022-01092-0
2022
Instance segmentation of 3D woven fabric from tomography images by Deep Learning and morphological pseudo-labeling DOI : 10.1016/j.compositesb.2022.110333
2022
APPLYING DEEP LEARNING TO MELANOCYTE COUNTING ON FLUORESCENT TRP1 LABELLED IMAGES OF IN VITRO SKIN MODEL DOI : 10.5566/ias.2640
2022
DARTBOARD BASED GROUND DETECTION ON 3D POINT CLOUD DOI : 10.5194/isprs-archives-XLIII-B2-2022-185-2022
2022
Fixed Point Layers for Geodesic Morphological Operations
2022
Learnable Empirical Mode Decomposition based on Mathematical Morphology DOI : 10.1137/21M1417867
2022
Morphological Adjunctions Represented by Matrices in Max-Plus Algebra for Signal and Image Processing DOI : 10.1007/978-3-031-19897-7_17
2022
DIFFERENTIAL INVARIANTS FOR SE(2)-EQUIVARIANT NETWORKS DOI : 10.1109/ICIP46576.2022.9897301
2022
Adaptive Anisotropic Morphological Filtering Based on Co-Circularity of Local Orientations DOI : 10.5201/ipol.2022.397
2022
MorphoActivation: Generalizing ReLU Activation Function by Mathematical Morphology DOI : 10.1007/978-3-031-19897-7_35
2021
Paris-carla-3d: A real and synthetic outdoor point cloud dataset for challenging tasks in 3d mapping DOI : 10.3390/rs13224713
2021
Measuring the Irregularity of Vector-Valued Morphological Operators Using Wasserstein Metric DOI : 10.1007/978-3-030-76657-3_37
2021
Nnakf: A Neural Network Adapted Kalman Filter for Target Tracking DOI : 10.1109/ICASSP39728.2021.9414681
2021
On power jaccard losses for semantic segmentation
2021
End-to-End Similarity Learning and Hierarchical Clustering for Unfixed Size Datasets DOI : 10.1007/978-3-030-80209-7_64
2021
Scale Equivariant Neural Networks with Morphological Scale-Spaces DOI : 10.1007/978-3-030-76657-3_35
2020
From unsupervised to semi-supervised anomaly detection methods for HRRP targets DOI : 10.1109/RadarConf2043947.2020.9266497
2020
On minimum spanning tree streaming for hierarchical segmentation DOI : 10.1016/j.patrec.2020.07.006
2020
SHREC 2020: 3D point cloud semantic segmentation for street scenes DOI : 10.1016/j.cag.2020.09.006
2020
SHREC’20 Track: Retrieval of digital surfaces with similar geometric reliefs DOI : 10.1016/j.cag.2020.07.011
2020
Road Segmentation on Low Resolution Lidar Point Clouds for Autonomous Vehicles DOI : 10.5194/isprs-annals-V-2-2020-335-2020
2020
Combinatorial space of watershed hierarchies for image characterization DOI : 10.1016/j.patrec.2019.11.002
2019
Part-based approximations for morphological operators using asymmetric auto-encoders DOI : 10.1007/978-3-030-20867-7_25
2019
A new color augmentation method for deep learning segmentation of histological images DOI : 10.1109/ISBI.2019.8759591
2019
A graph-based color lines model for image analysis DOI : 10.1007/978-3-030-30645-8_17
2019
Max-plus operators applied to filter selection and model pruning in neural networks DOI : 10.1007/978-3-030-20867-7_24
2019
The strong gravitational lens finding challenge DOI : 10.1051/0004-6361/201832797
2018
Characterizing Images by the Gromov-Hausdorff Distances between Derived Hierarchies DOI : 10.1109/ICIP.2018.8451522
2018
Tropical and Morphological Operators for Signals on Graphs DOI : 10.1109/ICIP.2018.8451395
2018
On minimum spanning tree streaming for image analysis DOI : 10.1109/ICIP.2018.8451715
2018
SHREC'18 track: Retrieval of gray patterns depicted on 3D models DOI : 10.2312/3dor.20181054
2018
SHREC'18 track: Recognition of geometric patterns over 3D models DOI : 10.2312/3dor.20181055
2018
On-the-go grapevine yield estimation using image analysis and boolean model DOI : 10.1155/2018/9634752
2018
Deep learning for galaxy surface brightness profile fitting DOI : 10.1093/mnras/stx3186
2018
Manipulating the alpha level cannot cure significance testing DOI : 10.3389/fpsyg.2018.00699
2018
Dealing with Topological Information Within a Fully Convolutional Neural Network DOI : 10.1007/978-3-030-01449-0_39
2017
Non-Negative Sparse Mathematical Morphology DOI : 10.1016/bs.aiep.2017.07.001
2017
SHREC'17 track: Point-cloud shape retrieval of non-rigid toys DOI : 10.2312/3dor.20171056
2017
preface
2017
SHREC'17 track: Retrieval of surfaces with similar relief patterns DOI : 10.2312/3dor.20171058
2017
Prior-based hierarchical segmentation highlighting structures of interest DOI : 10.1007/978-3-319-57240-6_12
2017
Morphological semigroups and scale-spaces on ultrametric spaces DOI : 10.1007/978-3-319-57240-6_3
2016
Retrieval and classification methods for textured 3D models: a comparative study DOI : 10.1007/s00371-015-1146-3
2016
Shrec'16 Track: Retrieval of Human Subjects from Depth Sensor Data DOI : 10.2312/3dor.20161086
2016
Automatic selection of stochastic watershed hierarchies DOI : 10.1109/EUSIPCO.2016.7760574
2016
Deep learning for studies of galaxy morphology DOI : 10.1017/S1743921317000552
2016
A Bayesian approach to linear unmixing in the presence of highly mixed spectra DOI : 10.1007/978-3-319-48680-2_24
2015
Inner-cheeger opening and applications DOI : 10.1007/978-3-319-18720-4_7
2015
Nonlinear operators on graphs via stacks DOI : 10.1007/978-3-319-25040-3_70
2015
Comparative analysis of covariance matrix estimation for anomaly detection in hyperspectral images DOI : 10.1109/JSTSP.2015.2442213
2015
Objects co-segmentation: Propagated from simpler images DOI : 10.1109/ICASSP.2015.7178257
2014
A comparative analysis of covariance matrix estimation in anomaly detection DOI : 10.1109/WHISPERS.2014.8077605
2014
Vector ordering and multispectral morphological image processing DOI : 10.1007/978-94-007-7584-8_7
2014
Robust anomaly detection in Hyperspectral Imaging DOI : 10.1109/IGARSS.2014.6947518
2014
Conditional toggle mappings: Principles and applications DOI : 10.1007/s10851-013-0429-4
2014
Local mutual information for dissimilarity-based image segmentation DOI : 10.1007/s10851-013-0432-9
2014
SHREC'14 track: Retrieval and classification on textured 3D models DOI : 10.2312/3DOR.20141057
2014
Riemannian mathematical morphology DOI : 10.1016/j.patrec.2014.05.015
2014
Anomaly detection and important bands selection for hyperspectral images via sparse PCA DOI : 10.1109/WHISPERS.2014.8077604
2013
Classification of hyperspectral images by tensor modeling and additive morphological decomposition DOI : 10.1016/j.patcog.2012.08.011
2013
On nonlocal mathematical morphology DOI : 10.1007/978-3-642-38294-9_19
2013
Multivariate diffusion tensor and induced segmentation DOI : 10.1109/WHISPERS.2013.8080638
2013
Shrec'13 track: Retrieval on textured 3D models DOI : 10.2312/3DOR/3DOR13/073-080
2013
Stochastic morphological filtering and Bellman-Maslov chains DOI : 10.1007/978-3-642-38294-9_15
2013
Mathematical morphology for real-valued images on Riemannian manifolds DOI : 10.1007/978-3-642-38294-9_24
2013
Complete lattice structure of Poincaré upper-half plane and mathematical morphology for hyperbolic-valued images DOI : 10.1007/978-3-642-40020-9_59
2013
Supervised morphology for structure tensor-valued images based on symmetric divergence kernels DOI : 10.1007/978-3-642-40020-9_60
2012
Robust RX anomaly detector without covariance matrix estimation DOI : 10.1109/WHISPERS.2012.6874301
2012
Edge extraction by statistical dependence analysis: Application to multi-angular WorldView-2 series DOI : 10.1109/IGARSS.2012.6350679
2012
Random projection depth for multivariate mathematical morphology DOI : 10.1109/JSTSP.2012.2211336
2011
Using biophotonics techniques to retrieve prognostic intracellular signatures; [La biophotonique au service de l'identification de marqueurs pronostiques intracellulaires] DOI : 10.1016/j.irbm.2011.01.039
2011
Supervised ordering in IRp: Application to morphological processing of hyperspectral images DOI : 10.1109/TIP.2011.2144611
2011
Multiclass ordering for filtering and classification of hyperspectral images DOI : 10.1109/WHISPERS.2011.6080922
2011
Sparse mathematical morphology using non-negative matrix factorization DOI : 10.1007/978-3-642-21569-8_1
2011
Mathematical morphology for vector images using statistical depth DOI : 10.1007/978-3-642-21569-8_31
2011
Structurally adaptive mathematical morphology based on nonlinear scale-space decompositions DOI : 10.5566/ias.v30.p111-122
2010
Spatial structures detection in hyperspectral images using mathematical morphology DOI : 10.1109/WHISPERS.2010.5594961
2010
Parameters selection of morphological scale-space decomposition for hyperspectral images using tensor modeling DOI : 10.1117/12.850171
2010
Statistical shape modeling using morphological representations DOI : 10.1109/ICPR.2010.863
2010
Semi-supervised hyperspectral image segmentation using regionalized stochastic watershed DOI : 10.1117/12.850187
2010
Morphological processing of hyperspectral images using kriging-based supervised ordering DOI : 10.1109/ICIP.2010.5651305
2010
Structurally adaptive mathematical morphology on nonlinear scale-space representations DOI : 10.1109/ICIP.2010.5651969
2010
Hit-or-miss transform in multivariate images DOI : 10.1007/978-3-642-17688-3_42
2009
Morphological scale-space for hyperspectral images and dimensionality exploration using Tensor modeling DOI : 10.1109/WHISPERS.2009.5289059
2009
Multiscale stochastic watershed for unsupervised hyperspectral image segmentation DOI : 10.1109/IGARSS.2009.5418095
2009
Morphological image distances for hyperspectral dimensionality exploration using kernel-PCA and ISOMAP DOI : 10.1109/IGARSS.2009.5418063
2009
Accelerating hyperspectral manifold learning using graphical processing units DOI : 10.1117/12.820176
2009
Improving hyperspectral image classification using spatial preprocessing DOI : 10.1109/LGRS.2009.2012443
2008
Improving hyperspectral image classification based on graphs using spatial preprocessing DOI : 10.1109/IGARSS.2008.4779433

Teaching

Deep Learning for Image Analysis

Lecturer

The course will consist of lectures and hands-on sessions. The lectures will cover: - Introduction to machine learning. - Artificial neural networks, backpropagation algorithm - Convolutional neural networks - Successful architectures - Analysis of neural network functionality - Image classification and segmentation - Autoencoders and generative networks - Current trends and research prospects During the practical sessions, students will implement the concepts learned in class using Python. They will tackle practical problems related to deep learning: architecture design, optimization strategies, hyperparameter selection, and analysis of results.

PhD supervision

2025 Robustness by design for frugal and trustworthy learning models LOZA RAMIREZ Edgar
2025 Generative 3D Models with Geometric Constraints OLECH Alexandre
2024 Hybrid image processing for surface particulate contamination metrology: coupling morphological methods and learning methods for aerosols PILLARD Dorian
2024 Morphological layers in neural networks: how to train and use them for data analysis DIMITROVA Mihaela
2024 Long-range maritime object tracking in the NIR band using artificial intelligence GOLEBIEWSKI Adrien
2023 Deep Learning on High-Resolution Radar Profiles: GANs and XAI BRIENT Edwyn
2023 A Priori Geometric Knowledge for Deep Learning Models in 3D Point Clouds ONGHENA Pierre
2023 The contribution of artificial intelligence to improving the performance of atmospheric contamination monitors ROBLIN Arthur
2021 Contributions to unsupervised visual anomaly detection CASAGRANDE BERTOLDO Joao Paulo
2021 Anomaly detection in satellite trajectories using artificial intelligence for space surveillance radar BAUDIER Stéfan
2020 Contributions to Equivariance to Roto-Translations for Deep Learning in Image Processing PENAUD--POLGE Valentin
2019 Using machine learning methods for radar tracking and aircraft classification tasks. JOUABER Sami
2019 One-class classification for low-resolution, weakly supervised discrimination of pulsed Doppler radar targets BAUW Martin
2019 Deep Learning Equivariant Based on Scale Spaces and Moving Frames SANGALLI Mateus
2018 3D urban scene understanding through analysis of LiDAR, color, and hyperspectral data DUQUE David
2017 Contributions to graph-based hierarchical analysis for images and 3D point clouds GIGLI Leonardo