International Conferences
"A Non-Parametric Spectral Model for Graph Classification".
Andrea Gasparetto, Giorgia Minello and Andrea Torsello.
The IV International Conference on Pattern Recognition Applications and Methods, (ICPRAM 2015).
January 2015, Lisbon, Portugal
Andrea Gasparetto, Giorgia Minello and Andrea Torsello.
The IV International Conference on Pattern Recognition Applications and Methods, (ICPRAM 2015).
January 2015, Lisbon, Portugal
"Transitive State Alignment for the Quantum Jensen-Shannon Kernel".
Andrea Torsello, Andrea Gasparetto, Luca Rossi, Lu Bai and Edwin R. Hancock.
Structural and Syntactic Pattern Recognition, (S+SSPR 2014).
August 2014, Joensuu, Finland
Andrea Torsello, Andrea Gasparetto, Luca Rossi, Lu Bai and Edwin R. Hancock.
Structural and Syntactic Pattern Recognition, (S+SSPR 2014).
August 2014, Joensuu, Finland
Master Thesis
"A Statistical Model of Riemmannian Metric Variation for Deformable Shape Analysis".
Non-rigid transformations problems have recently been largely addressed by the researchers community due to their importance in various areas, such as medical research and automatic information retrieval systems. In this dissertation we use a novel technique to learn a statistical model based on Riemmannian metric variation on deformable shapes. The variations learned over different datasets are then used to build a statistical model of a certain shape that is independent from the pose of the shape itself. The statistical model can then be used to classify shapes that do not belong to the original dataset.
Non-rigid transformations problems have recently been largely addressed by the researchers community due to their importance in various areas, such as medical research and automatic information retrieval systems. In this dissertation we use a novel technique to learn a statistical model based on Riemmannian metric variation on deformable shapes. The variations learned over different datasets are then used to build a statistical model of a certain shape that is independent from the pose of the shape itself. The statistical model can then be used to classify shapes that do not belong to the original dataset.