Full Professor at Ca’ Foscari University of Venice
Marcello Pelillo is a Full Professor of Computer Science at Ca’ Foscari University of Venice, Italy, where he leads the Computer Vision and Pattern Recognition group. He has directed the European Centre for Living Technology (ECLT) and has held visiting research/teaching positions in various institutions such as Yale University, McGill University, the University of Vienna, York University (UK), National ICT Australia (NICTA), Wuhan University, Huazhong University of Science and Technology, and South China University of Technology. He has been General Chair for ICCV 2017, Program Chair for ICPR 2020, and serves regularly as Area or Track Chair for the major conferences of his research areas (ICCV, ECCV, ICPR, BMVC, etc.). He is the Specialty Chief Editor of Frontiers in Computer Vision and serves, or has served, on the Editorial Boards of several journals, including IEEE Transactions on Pattern Analysis and Machine Intelligence, IET Computer Vision, Pattern Recognition, and Brain Informatics. He also serves on the Advisory Board of the International Journal of Machine Learning and Cybernetics. Prof. Pelillo is a Fellow of IEEE, IAPR, and AAIA, and is an IEEE SMC Distinguished Lecturer. His Erdös number is 2.
Graph-theoretic Methods in Computer Vision: Recent Advances
Graphs and graph-based representations have long been an important tool in computer vision and pattern recognition, especially because of their representational power and flexibility. There is now a renewed interest toward explicitly formulating computer vision problems as graph problems. This is particularly advantageous because it allows vision problems to be cast in a pure, abstract setting with solid theoretical underpinnings and also permits access to the full arsenal of graph algorithms developed in computer science and operations research. In this talk I’ll describe some recent developments in graph-theoretic methods which allow us to address within a unified and principled framework a number of classical computer vision problems. These include interactive image segmentation, image geo-localization, image retrieval, multi-camera tracking, and person re- identification. The concepts discussed here have intriguing connections with optimization theory, game theory and dynamical systems theory, and can be applied to weighted graphs, digraphs and