Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces M Ha Quang, M San Biagio, V Murino Advances in neural information processing systems 27, 2014 | 100 | 2014 |
Kernel-based classification for brain connectivity graphs on the Riemannian manifold of positive definite matrices L Dodero, HQ Minh, M San Biagio, V Murino, D Sona 2015 IEEE 12th international symposium on biomedical imaging (ISBI), 42-45, 2015 | 65 | 2015 |
Kernelized covariance for action recognition J Cavazza, A Zunino, M San Biagio, V Murino 2016 23rd International Conference on Pattern Recognition (ICPR), 408-413, 2016 | 54 | 2016 |
Heterogeneous Auto-Similarities of Characteristics (HASC) - Exploiting Relational Information for Classification M San Biagio, M Crocco, M Cristani, S Martelli, V Murino Computer Vision (ICCV), 2013 IEEE International Conference on 1, 809 - 816, 2013 | 54 | 2013 |
Approximate log-Hilbert-Schmidt distances between covariance operators for image classification HQ Minh, MS Biagio, L Bazzani, V Murino Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 24 | 2016 |
Exploiting feature correlations by Brownian statistics for people detection and recognition S Bąk, M San Biagio, R Kumar, V Murino, F Brémond IEEE Transactions on Systems, Man, and Cybernetics: Systems 47 (9), 2538-2549, 2016 | 21 | 2016 |
Automatic inspection of aeronautic components MS Biagio, C Beltran-Gonzalez, S Giunta, AD Bue, V Murino Machine Vision and Applications 28, 591-605, 2017 | 16 | 2017 |
Weighted bag of visual words for object recognition M San Biagio, L Bazzani, M Cristani, V Murino 2014 IEEE International Conference on Image Processing (ICIP), 2734-2738, 2014 | 14 | 2014 |
A new SOCMINT framework for Threat Intelligence Identification M San Biagio, R Acquaviva, V Mazzonello, E La Mattina, V Morreale 2021 International Conference on Computational Science and Computational …, 2021 | 6 | 2021 |
Kernel methods on approximate infinite-dimensional covariance operators for image classification HQ Minh, MS Biagio, L Bazzani, V Murino arXiv preprint arXiv:1609.09251, 2016 | 6 | 2016 |
Encoding structural similarity by cross-covariance tensors for image classification M San Biagio, S Martelli, M Crocco, M Cristani, V Murino International Journal of Pattern Recognition and Artificial Intelligence 28 …, 2014 | 6 | 2014 |
Low-level multimodal integration on riemannian manifolds for automatic pedestrian detection M San-Biagio, M Crocco, M Cristani, S Martelli, V Murino 2012 15th International Conference on Information Fusion, 2223-2229, 2012 | 6 | 2012 |
Recursive segmentation based on higher order statistics in thermal imaging pedestrian detection M San-Biagio, M Crocco, M Cristani 2012 5th International Symposium on Communications, Control and Signal …, 2012 | 6 | 2012 |
A multiple kernel learning approach to multi-modal pedestrian classification M San-Biagio, A Ulaş, M Crocco, M Cristani, U Castellani, V Murino Proceedings of the 21st International Conference on Pattern Recognition …, 2012 | 5 | 2012 |
Encoding classes of unaligned objects using structural similarity cross-covariance tensors M San Biagio, S Martelli, M Crocco, M Cristani, V Murino Progress in Pattern Recognition, Image Analysis, Computer Vision, and …, 2013 | 4 | 2013 |
MARPLE: A Framework for Social Media Threat Intelligence M San Biagio, S Simoncini, E La Mattina, V Morreale 2024 International Conference on Artificial Intelligence, Computer, Data …, 2024 | 2 | 2024 |
Surrogate modeling for injection molding processes using deep learning A Uglov, S Nikolaev, S Belov, D Padalitsa, T Greenkina, MS Biagio, ... Structural and Multidisciplinary Optimization 65 (11), 305, 2022 | 1 | 2022 |
THINT: a Deep Learning Framework for Terrorist Threats Identification in Social Media M San Biagio, V Scarfone, E La Mattina, V Morreale 2024 International Conference on Electrical, Computer and Energy …, 2024 | | 2024 |
A Topic Clustering Method to Identify Online Threats against Soft Targets M San Biagio, M Cipolla, E La Mattina, V Morreale 2023 International Conference on Computational Science and Computational …, 2023 | | 2023 |
Surrogate Modelling for Injection Molding Processes using Machine Learning A Uglov, S Nikolaev, S Belov, D Padalitsa, T Greenkina, MS Biagio, ... arXiv preprint arXiv:2107.14574, 2021 | | 2021 |