Computer Vision and Deep Learning
Computer vision and deep learning are powerful tools that can be used to extract meaningful information from unstructured data and perform complex tasks with high accuracy. We are working on computer vision and deep learning solutions in several areas, including digital humanities and drone vision. These solutions can help analyze large visual art collections in digital humanities, providing new insights into history, culture, and society. In drone vision, they can be used to analyze and process images captured by drones, providing valuable information for various applications such as agriculture.
Some recent publications
Castellano, G., Esposito, A., Lella, E., Montanaro, G., Vessio, G. (2024). Automated detection of Alzheimer's disease: a multi-modal approach with 3D MRI and amyloid PET. Scientific Reports.Â
Castellano, G., De Marinis, P., & Vessio, G. (2023). Weed mapping in multispectral drone imagery using lightweight vision transformers. Neurocomputing.
Castellano, G., Cotardo, E., Mencar, C., & Vessio, G. (2023). Density-based clustering with fully-convolutional networks for crowd flow detection from drones. Neurocomputing.
Castellano, G., & Vessio, G. (2022). A deep learning approach to clustering visual arts. International Journal of Computer Vision.
Castellano, G., Digeno, V., Sansaro, G., & Vessio, G. (2022). Leveraging knowledge graphs and deep learning for automatic art analysis. Knowledge-Based Systems.
Castellano, G., & Vessio, G. (2021). Deep learning approaches to pattern extraction and recognition in paintings and drawings: An overview. Neural Computing and Applications.
Castellano, G., Lella, E., & Vessio, G. (2021). Visual link retrieval and knowledge discovery in painting datasets. Multimedia Tools and Applications.
Castellano, G., Castiello, C., Mencar, C., & Vessio, G. (2020). Crowd detection in aerial images using spatial graphs and fully-convolutional neural networks. IEEE Access.