The field of computer vision deals with the acquisition, processing, and analysis of digital images in order to develop applications for object detection, segmentation, and vision based learning tasks. The objective is to extract information from images and videos, which may come from various sensors (such as multiple cameras, medical scanners), using traditional and deep learning techniques. Studied topics include: scene reconstruction, event detection, video tracking, object recognition, writing analysis, and image restoration.

• Computer vision for event detection and tracking
• Structural damage detection
• Gesture recognition

Applications of Computer Vision for cultural heritage can be found under Digital Humanities.

Furthermore, in the past:

• Automatic screening of digital slides
• Vehicle-based mobile mapping system
• Proteomics
• Hierarchical architectures

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Latest publications

• Simona Zlatanova, Piercarlo Dondi, Marco Porta, Michele Antolini (2025). Optimizing PatchCore for One-Shot Industrial Anomaly Detection, 2025 IEEE 30^th International Conference on Emerging Technologies and Factory Automation (ETFA), Porto (Portugal), 9-12 September 2025, pp. 1-4, DOI:10.1109/ETFA65518.2025.11205697.
• Piercarlo Dondi, Alessio Gullotti, Michele Inchingolo, Ilaria Senaldi, Chiara Casarotti, Luca Lombardi, Marco Piastra (2025). Post-earthquake structural damage detection with tunable semi-synthetic image generation, in Engineering Applications of Artificial Intelligence, Volume 147, 1 May 2025, 110302, Elsevier, DOI:10.1016/j.engappai.2025.110302.
• Piercarlo Dondi, Alessio Gullotti, Michele Inchingolo, Ilaria Senaldi, Chiara Casarotti, Luca Lombardi, Marco Piastra (2025). Improving Post-Earthquake Crack Detection using Semi-Synthetic Generated Images, in Del Bue A., Canton C., Pont-Tuset J., Tommasi T. (eds), Computer Vision – ECCV 2024 Workshops. ECCV 2024, Lecture Notes in Computer Science, vol 15642, Springer, Cham, DOI:10.1007/978-3-031-91907-7_2.
• Piercarlo Dondi, Ilaria Senaldi, Luca Lombardi, Marco Piastra (2023). Many-to-Many metrics: a new approach to evaluate the performance of structural damage detection networks, in Foresti G.L., Fusiello A., Hancock E. (eds.), Image Analysis and Processing – ICIAP 2023, Lecture Notes in Computer Science, vol 14234, pp. 144-155, Springer, Cham, DOI:10.1007/978-3-031-43153-1_13.