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 distribution of the data used during the training of a model.

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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 volumetric images stimulate the research in immersive analytics.

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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, processing and analysis of a large variety of healthcare data.

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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 in nearly every area of data science.

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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 related to the effects of occlusions, retrieval from large databases, and multi-modal representations.

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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.

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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 same person appearing in a different pose, from a different viewpoint, and under different illumination conditions.

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Activity Recognition

Providing a semantic interpretation of the actions and interactions between the actors in a scene enables behavior analysis for automated decision-making. This is still a challenging problem, especially when performed online.

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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 is still a challenging computer vision task.

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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 to follow, and they may look similar and overlap as they move or deform over time.

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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-based rendering, recognition, or segmentation, just to mention a few broad areas), several texture models have been proposed in the literature.

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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 3D synthetic environments. One way to build models is by acquiring range data images, by means of laser scanners, and then stitching them together.

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