20 citations · 30 across the 8 of their papers we have counts for
9 papers
Superquadric Primitive Decomposition of 3D point clouds via Geometric-Aware Inlier Refinement
Alessandro Rinaldi, Edoardo Tedesco, Andrea Ferraris +5
The decomposition of 3D point clouds into interpretable geometric primitives remains a longstanding challenge in Computer Vision and Computer Graphics. Among the available represen…
Structure-based Anomaly Detection and Clustering
Filippo Leveni
Anomaly detection is a fundamental problem in domains such as healthcare, manufacturing, and cybersecurity. This thesis proposes new unsupervised methods for anomaly detection in b…
Malware families discovery via Open-Set Recognition on Android manifest permissions
Filippo Leveni, Matteo Mistura, Francesco Iubatti +4
Malware are malicious programs that are grouped into families based on their penetration technique, source code, and other characteristics. Classifying malware programs into their…
MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model Selection
Luca Magri, Filippo Leveni, Giacomo Boracchi
We address the problem of recovering multiple structures of different classes in a dataset contaminated by noise and outliers. In particular, we consider geometric structures defin…
Hashing for Structure-based Anomaly Detection
Filippo Leveni, Luca Magri, Cesare Alippi +1
We focus on the problem of identifying samples in a set that do not conform to structured patterns represented by low-dimensional manifolds. An effective way to solve this problem…
PIF: Anomaly detection via preference embedding
Filippo Leveni, Luca Magri, Giacomo Boracchi +1
We address the problem of detecting anomalies with respect to structured patterns. To this end, we conceive a novel anomaly detection method called PIF, that combines the advantage…
Online Isolation Forest
Filippo Leveni, Guilherme Weigert Cassales, Bernhard Pfahringer +2
The anomaly detection literature is abundant with offline methods, which require repeated access to data in memory, and impose impractical assumptions when applied to a streaming c…
Preference Isolation Forest for Structure-based Anomaly Detection
Filippo Leveni, Luca Magri, Cesare Alippi +1
We address the problem of detecting anomalies as samples that do not conform to structured patterns represented by low-dimensional manifolds. To this end, we conceive a general ano…
Change Detection in Multivariate data streams: Online Analysis with Kernel-QuantTree
Michelangelo Olmo Nogara Notarianni, Filippo Leveni, Diego Stucchi +2
We present Kernel-QuantTree Exponentially Weighted Moving Average (KQT-EWMA), a non-parametric change-detection algorithm that combines the Kernel-QuantTree (KQT) histogram and the…