Pages
Home
Publications
Publications
Code
Code

Santiago VELASCO-FORERO

HDR

Contributions to Mathematical Morphology

Jury:
Isabelle BLOCH, Professeur, Sorbonne Université, Paris, Présidente
Pierre SOILLE, Project Leader, HDR, Joint Research Centre of the European Commission
Yann GOUSSEAU, Professeur, Télécom Paris
Nicolas PASSAT, Professeur, Université de Reims Champagne-Ardenne
Jean-Michel MOREL, Professeur, École Normale Supérieure, Paris-Saclay
Stephane MALLAT, Professeur, Collège de France Paris

Thesis

Topics in mathematical morphology for multivariate images

Director:
Jesús ANGULO, Chargé de recherche, CMM, Mines ParisTech

Jury:
Dominique JEULIN, Professeur, CMM-MS, Mines ParisTech (Président)
Jos B.T.M. ROERDINK, Professeur, University of Groningen (Rapporteur)
Pierre SOILLE, Directeur de recherche, Joint Research Centre of the European Commission (Rapporteur)
Jean SERRA, Professeur émérite, ESIEE, Université Paris-Est (Examinateur)
Jón Atli BENEDIKTSSON, Professeur, University of Iceland (Examinateur)
Fernand MEYER, Directeur de recherche, CMM, Mines ParisTech (Examinateur)

Editor

  1. 13th International Symposium, ISMM 2017, Fontainebleau, France, May 15–17, 2017, Proceedings

Journal papers

  1. Adaptive anisotropic morphological filtering based on co-circularity of local orientations, Image Processing On Line, IPOL -Image Processing on Line, 2022

  2. Irregularity Index for Vector-Valued Morphological Operators, Journal of Mathematical Imaging and Vision, 2022.

  3. Learnable Empirical Mode Decomposition based on Mathematical Morphology, SIAM Journal on Imaging Sciences, 15, 1, 2022.

  4. Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping, Remote Sensing,13 (22), 2021.

  5. On minimum spanning tree streaming for hierarchical segmentation, Pattern Recognition Letters 138, 155-162, 2020

  6. SHREC 2020 Track: 3D Point Cloud Semantic Segmentation for Street Scenes, Computer and Graphics, 2020

  7. SHREC’20 track: Retrieval of digital surfaces with similar geometric reliefs, Computers and Graphics, 2020

  8. Approximating morphological operators with part-based representations learned by asymmetric auto-encoders, Mathematical Morphology-Theory and Applications, 2020

  9. Combinatorial space of watershed hierarchies for image characterization, Pattern Recognition Letters, 2020, 41-47

  10. Prior-based Hierarchical Segmentation Highlighting Structures of Interest, Mathematical Morphology-Theory and Applications, 2019

  11. The strong gravitational lens finding challenge, Astronomy and Astrophysics, 2019

  12. On-the-go grapevine yield estimation using image analysis and Boolean model, Journal of Sensors, 2018

  13. Manipulating the alpha level cannot cure significance testing, D. Trafimow et al., Frontiers in Psychology, 2018

  14. Deep learning for galaxy surface brightness profile fitting, D. Tuccillo, M. Huertas-Company, E. Decenciére, S. Velasco-Forero, H. Domínguez-Sánchez and P. Dimauro, Monthly Notices of the Royal Astronomical Society, Dec., 2017

  15. Non-Negative Sparse Mathematical Morphology, J. Angulo and S. Velasco-Forero, Advances in Imaging and Electron Physics 202, 1-37, 2017.

  16. Retrieval and classification methods for textured 3D models: A comparative study, S.Biasotti, M. Aono, A. Ben Hamza, V. Garro, A. Giachetti, D. Giorgi, A. Godil, C. Li, C. Sanada, M. Spagnuolo, A. Tatsuma and S. Velasco-Forero, 1–25, August, 2015, The Visual Computer Journal.

  17. Comparative Analysis of Covariance Matrix Estimation for Anomaly Detection in Hyperspectral Images, S. Velasco-Forero, M.Chen, A. Goh and S.K. Pang, Volume 9, Nro. 6, Sept 2015, 1061–1073, IEEE Journal of Selected Topics in Signal Processing.

  18. Riemannian mathematical morphology, J. Angulo and S. Velasco-Forero, Volume 47, Nro. 1, October 2014, 93–101, Pattern Recognition Letters.

  19. Conditional toggle mappings: principles and applications, S. Velasco-Forero, J. Angulo and P. Soille, March 2014, Volume 48, Issue 3, pp. 544-565, Journal of Mathematical Imaging and Vision.

  20. Local mutual information for dissimilarity based image segmentation, L. Gueguen, S. Velasco-Forero and P. Soille, March 2014, Volume 48, Issue 3, pp 625-644, Journal of Mathematical Imaging and Vision.

  21. Classification of hyperspectral images by tensor modeling and additive morphological decomposition, S. Velasco-Forero and J. Angulo, vol. 46, num. 2, Feb. 2013, Pattern Recognition.

  22. Random projection depth for multivariate mathematical morphology, S. Velasco-Forero and J. Angulo, vol. 6, num. 7, Oct. 2012, IEEE Journal of Selected Topics in Signal Processing.

  23. Supervised ordering in R^p: Application to morphological processing of hyperspectral images, S. Velasco-Forero and J. Angulo, vol. 20, num. 11, Oct. 2011, IEEE Transactions on Image Processing.

  24. Structurally adaptive mathematical morphology, J. Angulo and S. Velasco-Forero, num. 30, 101-112, 2011, Image Analysis and Stereology.

  25. Improving hyperspectral image classification using spatial preprocessing, S. Velasco-Forero and V. Manian, vol.6, num. 2, pp. 297–301, 2008, IEEE Geoscience and Remote Sensing Letters.

Chapter books

  1. Morphological processing of univariate Gaussian distribution valued images based on Poincarè upper-half plane representation, J. Angulo and S. Velasco-Forero, Geometric Theory of Information Signals and Communication Technology, pp. 331-366, 2014.

  2. Vector Ordering and Multispectral Morphological Image Processing, J. Angulo and S. Velasco-Forero, Advances in Low-Level Color Image Processing, Signals and Communication Technology, pp. 223-239, 2014.

In French

  1. La biophotonique au service de l‘identification de marqueurs pronostiques intracellulaires, J. Klossa et al., vol. 32, Issue 2, pp. 72-75, 2011.

Lectures Notes in Computer Science

  1. Scale Equivariant Neural Networks with Morphological Scale-Spaces, DGMM 2021.

  2. Measuring the Irregularity of Vector-valued Morphological Operators using Wasserstein Metric, DGMM 2021.

  3. A graph-based color lines model for image analysis, D. Duque-Arias, S. Velasco-ForeroJ.-E. DeschaudF. GouletteB. Marcotegui, ICIAP 2019

  4. Max-plus operators applied to filter selection and model pruning in neural networks, Y Zhang, S Blusseau, S Velasco-Forero, I Bloch, J Angulo, ISMM 2019.

  5. Part-based approximations for morphological operators using asymmetric auto-encoders, Bastien Ponchon, Santiago Velasco-Forero, Samy Blusseau, Jesus Angulo, and Isabelle Bloch, ISMM 2019

  6. Morphological Semigroups and Scale-Spaces on Ultrametric Spaces, J. Angulo and S. Velasco-Forero, ISMM 2017

  7. Prior-Based Hierarchical Segmentation Highlighting Structures of Interest, A. Fehri, S. Velasco-Forero, F. Meyer, ISMM 2017

  8. S. Velasco-Forero and J. Angulo, Nonlinear Operators on Graphs via Stacks, Geometric Science of Information, pp. 654–663, 2016.

  9. Inner-Cheeger Opening and Applications S. Velasco-Forero, Mathematical Morphology and Its Applications to Signal and Image Processing, LNCS, vol. 9082, pp. 75-85, 2015

  10. Supervised morphology for tensor structure-valued images based on symmetric divergence kernels S. Velasco-Forero and J. Angulo, in Geometric Science of Information, vol. 8085, pp. 543-550, 2013.

  11. On nonlocal mathematical morphology S. Velasco-Forero and J. Angulo, Mathematical Morphology and Its Applications to Signal and Image Processing, LNCS, vol. 7883, pp. 219-230, 2013.

  12. Mathematical morphology for real-valued images on Riemannian manifolds J. Angulo and S. Velasco-Forero, Mathematical Morphology and Its Applications to Signal and Image Processing, LNCS, vol.7883, pp. 279-291, 2013.

  13. Stochastic morphological filtering and Bellman-Maslov chains J. Angulo and S. Velasco-Forero, Mathematical Morphology and Its Applications to Signal and Image Processing, LNCS, vol. 7883, pp. 171-182, 2013.

  14. Mathematical morphology for vector images using statistical depth S. Velasco-Forero and J. Angulo, in Mathematical Morphology and Its Applications to Image and Signal Processing, LNCS, vol. 6671, pp. 355-366, 2011

  15. Sparse mathematical morphology using non-negative matrix factorization J. Angulo and S. Velasco-Forero, in Mathematical Morphology and Its Applications to Image and Signal Processing, LNCS, vol. 6671, pp. 1-12, 2011

  16. Hit-or-Miss Transform in Multivariate Images S.Velasco-Forero and J.Angulo in Advanced Concepts for Intelligent Vision Systems, vol. 6474, 2010, pp. 452-463, LNCS, 2010.

Conference Proceedings

  1. ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results, 2021.

  2. End-to-End Similarity Learning and Hierarchical Clustering for unfixed size datasets, International Conference on Geometric Science of Information,2021.

  3. NNAKF: A neural network adapted Kalman filter for target tracking. ICASSP 2021.

  4. On power Jaccard losses for semantic segmentation, VISAPP 2021.

  5. From unsupervised to semi-supervised anomaly detection methods for High Resolution Range Profiles, IEEE Radar Conference 2020.

  6. Road segmentation on low resolution lidar point clouds for autonomous vehicles, ISPRS2020

  7. A New Color Augmentation Method for Deep Learning Segmentation of Histological Images, ISBI 2019

  8. Dealing with Topological Information within a Fully Convolutional Neural Network, ACIVS 2018

  9. On minimum spanning tree streaming for image analysis, ICIP 2018.

  10. Characterizing images by Gromov-Hausdorff distances between derived hierarchies, ICIP 2018.

  11. Tropical and morphological operators for signals on graphs, ICIP 2018.

  12. SHREC’18 track: Recognition of geometric patterns over 3D models, 11th Eurographics Workshop on 3D Object Retrieval, 2018.

  13. SHREC’18 track: Retrieval of gray patterns depicted on 3D models, 11th Eurographics Workshop on 3D Object Retrieval, 2018.

  14. SHREC’17 Track: Point-Cloud Shape Retrieval of Non-Rigid Toys, FA Limberger, et al., 10th Eurographics Workshop on 3D Object Retrieval, 2017.

  15. SHREC’17 Track: Retrieval of surfaces with similar relief patterns, S Biasotti, et al., 10th Eurographics Workshop on 3D Object Retrieval, 2017.

  16. A Bayesian Approach to Linear Unmixing in the Presence of Highly Mixed Spectra, B Figliuzzi, S Velasco-Forero, M Bilodeau, J Angulo, 2016, ACIVS 2016: Advanced Concepts for Intelligent Vision Systems pp 263-274

  17. Shrec’16 Retrieval of Human Subjects from Depth Sensor Data, A. Giachetti et al., Eurographics Workshop on 3D Object Retrieval, 2016.

  18. Automatic selection of Stochastic Watershed Hierarchies, A. Fehri, S. Velasco-Forero and F. Meyer, EUSIPCO 2016.

  19. Objects co-segmentation: Propagated from simpler images M. Chen, S. Velasco-Forero, I. Tsang and T.J. Cham, IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 1682-1686, 2015

  20. Anomaly detection and important band selection for hyperspectral images via sparse PCA, S. Velasco-Forero, M. Chen, A. Goh and S.K. Pang, 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2014.

  21. A comparative analysis of covariance matrix estimation in anomaly detection, S. Velasco-Forero, M. Chen, A. Goh and S.K. Pang, 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2014.

  22. Robust Anomaly Detection in Hyperspectral imaging J. Frontera-Pons et al., IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4604-4607, 2014.

  23. SHREC-14 Track: Retrieval and classification on Textured 3D Models, S. Biasotti et al., accepted to Eurographics Workshop on 3D Object Retrieval, 2014.

  24. SHREC’13 Track: Retrieval on textured 3D models, A. Cerri et al., Eurographics Workshop on 3D Object Retrieval, pp. 73-80, 2013.

  25. Multivariate diffusion tensor and induced morphological segmentation, S. Velasco-Forero, M. Marin-Mc Gee and M. Vélez-Reyes, 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2013

  26. Robust RX Anomaly Detector without covariance matrix estimation, S.Velasco-Forero and J. Angulo, 2012 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), June, pp. 1-4, 2012.

  27. Edge Extraction by statistical dependence analysis: Application to multi-angular Worldview-2 series, L. Gueguen, S. Velasco-Forero and P. Soille, IGARSS International Geoscience and Remote Sensing Symposium, pp. 3447-3450, 2012.

  28. Multiclass ordering for filtering and classification of hyperspectral images, S. Velasco-Forero and J. Angulo, 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1-4, 2011.

  29. Spatial structures detection in hyperspectral images using mathematical morphology, S.Velasco-Forero and J.Angulo, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1-4, 2010.

  30. Morphological processing of hyperspectral images using kriging-based supervised ordering, S.Velasco-Forero and J.Angulo in IEEE-International Conference on Image Processing (ICIP)- Hong Kong, 2010, pp. 1409-1412.

  31. Structurally adaptive mathematical morphology on nonlinear scale-space representations, J. Angulo and S.Velasco-Forero, in IEEE-International Conference on Image Processing (ICIP)- Hong Kong 2010, pp. 121-124.

  32. Semi-supervised hyperspectral image segmentation using regionalized stochastic watershed, J.Angulo and S.Velasco-Forero, in SPIE symposium on SPIE Defense, Security, and Sensing, April, Orlando, USA, 2010.

  33. Statistical shape modeling using morphological representations, S.Velasco-Forero and J.Angulo,in 20th International Conference on Pattern Recognition (ICPR), Istambul, Turkey, 2010, pp. 3537-3540.

  34. Parameters selection of morphological scale-space decomposition for hyperspectral images using tensor modeling, S.Velasco-Forero and J.Angulo, in SPIE symposium on SPIE Defense, Security, and Sensing, April, Orlando, USA, 2010.

  35. Morphological image distances for hyperspectral dimensionality exploration using kernel-PCA and Isomap, S.Velasco-Forero, J.Angulo, and J.Chanussot,, in IEEE - International Geoscience and Remote Sensing Symposium, July, Cape Town, South Africa, 2009.

  36. Multiscale stochastic watershed for unsupervised hyperspectral image segmentation , J.Angulo, S.Velasco-Forero, and J.Chanussot, in IEEE International Geoscience and Remote Sensing Symposium, July, Cape Town,South Africa, 2009.

  37. Accelerating hyperspectral manifold learning using graphical processing units , S.Velasco-Forero and V.Manian, in SPIE Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, April, Orlando, USA, 2009.

  38. Morphological scale-space for hyperspectral images and dimensionality exploration using tensor modeling, S.Velasco-Forero and J.Angulo,, IEEE - Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, August, Grenoble, France, 2009.

  39. Improving hyperspectral image classification based on graphs using spatial preprocessing, S.Velasco-Forero and V.Manian, in IEEE - International Geosciences and Remote Sensing Symposium, 7-11 July, Boston, USA, 2008.