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ATENEO DI QUALITÀ ACCREDITATO ANVUR - FASCIA A

Image processing, Mobile vision and Pattern recognition lab (IMP Lab)

Team members
implab

http://implab.ce.unipr.it

The research activity of IMP Lab is related to Computer Vision, Image Processing and Pattern Recognition with large use of machine learning and deep learning techniques. The most active research activities are related to:

  • Geometric deep learning
  • Generative Adversarial network
  • Convolutional Neural network
  • Unsupervised, semi-supervised and self-supervised learning
  • Explainability and controllability of deep learning architectures

 
These researches have been applied to several projects, both with industries and related to national and international fundings.  
Current research is focused on:

  1. Generative models for unsupervised and controllable manipulation of factors of variations in images
  2. Mask-to-face generation with automatic learned mask swappings
  3. Teacher-student two-stage CNNs for instance segmentation 

 

Contact person: Andrea Prati – andrea.prati@unipr.it

Recent Research Projects
  • 2022-2024: Project “LEGO.AI: LEarning the Geometry of knOwledge in AI systems”, (funds of the Italian Minsitry of Research, PRIN 2020 programme, Universities of Roma "La Sapienza", Trento, Catania)
  • 2022: Project “ROADSTER: ROAd Digital Sustainable Twins in Emilia-Romagna: artificial intelligence for industrial areas” (funds Fondazione IFAB Call for Projects 2021)
Selected publications
  1. G. Gualdi, A. Prati, R. Cucchiara, “Multi-Stage Particle Windows for Fast and Accurate Object Detection”, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, n. 8, pp. 1589-1604, August 2012
  2. S. Calderara, A. Prati, R. Cucchiara, “Integrate tool for online analysis and offline mining of people trajectories”, in IET Computer Vision journal, vol. 6, n. 4, pp. 334-347, July 2012
  3. P. Piccinini, A. Prati, R. Cucchiara, “Real-time Object Detection and Localization with SIFT-based Clustering”, in Image and Vision Computing, vol. 30, n. 8, pp. 573-587, August 2012
  4. P. Piccinini, R. Gamberini, A. Prati, B. Rimini, R. Cucchiara, “An Automated Picking Workstation for Healthcare Applications”, in Computers & Industrial Engineering, vol. 64, n. 2, pp. 653-668, 2013
  5. M. Fornaciari, A. Prati, R. Cucchiara, “A Fast and Effective Ellipse Detector for Embedded Vision Applications”, in Pattern Recognition, vol. 47, pp. 3693-3708, 2014
  6. A. Prati, F. Qureshi, “Integrating Consumer Smart Cameras into Camera Networks: Opportunities and Obstacles”, in IEEE Computer, vol. 47, no. 5, pp. 45-51, 2014
  7. I. Huerta, C. Fernández, C. Segura, J. Hernando, A. Prati, “A deep analysis on age estimation”, in Pattern Recognition Letters, vol. 68, pag. 239-249, 2015
  8. Y.T. Tesfaye, E. Zemene, M. Pelillo, A. Prati, “Multi-object tracking using dominant sets”, in IET Computer Vision, vol. 10, no. 4, pp. 289-298, 2016
  9. V. Renò, N. Mosca, M. Nitta, T. D’Orazio, C. Guaragnella, D. Campagnoli, A. Prati, E. Stella, “A technology platform for automatic high-level tennis game analysis”, in Computer Vision and Image Understanding, vol. 159, pp. 164-175, June 2017
  10. E. Zemene, Y. Tariku, H. Idrees, A. Prati, M. Pelillo, M. Shah, “Large-scale Image Geo-Localization Using Dominant Sets”, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, n. 1, pp. 148-161, January 2019
  11. A. Prati, C. Shan, K. Wang, “Sensors, Vision and Networks: from video surveillance to activity recognition and health monitoring”, in Journal of Ambient Intelligence and Smart Environments, vol. 11, n. 1, pp. 5-22, January 2019 
  12. L. Donati, S. Cesano, A. Prati,   “A complete hand-drawn sketch vectorization framework”, in Multimedia Tools and Applications, vol. 78, no. 14, pp. 19083-19113, Springer Science+Business Media B.V, ISSN 1380-7501, 2019
  13. Y. Tariku, E. Zemene, A. Prati, M. Pelillo, M. Shah, “Multi-Target Tracking in Multiple Non-Overlapping Cameras using Fast-Constrained Dominant Sets”, in International Journal of Computer Vision, vol. 127, pp. 1303-1320, 2019 
  14. L. Donati, E. Iotti, G. Mordonini, A. Prati, “Fashion Product Classification Through Deep Learning and Computer Vision”, in MDPI Applied Sciences, vol. 9, n. 7, 2019
  15. T. Fontanini, E. Iotti, L. Donati, A. Prati, “MetalGAN: Multi-Domain Label-Less Image Synthesis Using cGANs and Meta-Learning”, in Special Issue on “Deep Neural Network Representation and Generative Adversarial Learning” of Neural Network journal, vol. 131, pp. 185-200, 2020
  16. F. Magliani, L. Sani, S. Cagnoni, A. Prati, “Diffusion Parameters Analysis in a Content-Based Image Retrieval task for Mobile Vision”, in Special Issue on “Cooperative Camera Networks” of MDPI Sensors journal, 20(16), 4449, August 2020
  17. L. Donati, T. Fontanini, F. Tagliaferri, A. Prati, “An energy saving road sweeper using deep vision for garbage detection”, in MDPI Applied Sciences, vol. 10, n. 22, 2020
  18. F. Magliani, T. Fontanini, A. Prati, “Bag of Indexes: a Multi-Index Scheme for Efficient Approximate Nearest Neighbor Search”, in Multimedia Tools and Applications, pp. 1-22, 2021
  19. L. Donati, E. Iotti, A. Prati, “A Real-Time Approach for Automatic Food Quality Assessment based on Shape Analysis”, in International Journal of Computational Intelligence and Applications, vol. 20, 2021
  20. F. Magliani, A. Prati, “LSH kNN Graph for Diffusion on Image Retrieval”, in Information Retrieval journal, 2021
Pubblicato Martedì, 22 Marzo, 2022 - 08:54 | ultima modifica Martedì, 22 Marzo, 2022 - 10:16