3D Object Modeling
Building 3D models is an important problem in several areas, such as forensic applications, medical applications, industrial inspection, or virtual visits of...
Dynamic Textures
One of the most important elements of modern Computer Vision is the concept of image texture, or simply texture. Depending on the task at hand (e.g. image-ba...
Video Tracking
Video tracking is the process of localizing an object in video at every time instant. It is a challenging problem especially when there are multiple objects ...
Object Detection
Object detection consists in automatically localizing and recognizing in images the presence of objects belonging to a predefined set of categories, and it i...
Activity Recognition
Providing a semantic interpretation of the actions and interactions between the actors in a scene enables behavior analysis for automated decision-making. Th...
Person Re-Identification
Long-duration tracking of individuals across large sites is a challenge. Trucks of individuals from disjoint fields of view need to be linked, despite the sa...
Plant Image Analysis
Modern precision agriculture takes advantage of image data collected from various platforms to monitor crops and optimize their management and maximize yield.
Face Recognition
Human identification based on facial traits has made remarkable progress thanks to deep learning-based methods, but there are still challenges, for example r...
Novelty and Anomaly Detection
Detecting the presence of outliers, like novelties or anomalies, with respect to a particular distribution has numerous applications in computer vision and i...
Biomedical and Health Data Analysis
The effort to improve healthcare delivery by making medicine more personalized, precise and efficient, presents many challenges that require the acquisition,...
Immersive Analytics
Recent advanecs in augmented, mixed, and virtual reality, coupled with the need to perform analysis and decision-making on large-scale collections of volumet...
Out-of-Distribution Learning
Out-of-Distribution learning is concerned with developing methods where the distribution of the data processed at test time may be different from the distrib...