Professor Head of the Computer Vision and Machine Learning Group
Prof. Petia Radeva is a Full professor at the Universitat de Barcelona (UB), PI of the Consolidated Research Group “Computer Vision and Machine Learning” at the University of Barcelona (CVUB) at UB (www.ub.edu/cvub) and Senior researcher in Computer Vision Center (www.cvc.uab.es).
She was PI of UB in 4 European, 3 international and more than 20 national projects devoted to applying Computer Vision and Machine learning for real problems like food intake monitoring (e.g. for patients with kidney transplants and for older people). Petia Radeva is a REA-FET-OPEN vice-chair since 2015 on, and international mentor in the Wild Cards EIT program since 2017. She is an Associate editor of Pattern Recognition journal (Q1) and International Journal of Visual Communication and Image Representation (Q2).
Petia Radeva has been awarded IAPR Fellow since 2015, ICREA Academia assigned to the 30 best scientists in Catalonia for her scientific merits since 2014, received several international awards (“Aurora Pons Porrata” of CIARP, Prize “Antonio Caparrós” for the best technology transfer of UB, etc).
She supervised 18 PhD students and published more than 100 SCI journal publications and 250 international chapters and proceedings, her Google scholar h-index is 44 with more than 7600 cites and WOS h-index: 79.
Uncertainty modeling for food analysis within end-to-end framework
Recently, computer vision approaches specially assisted by deep learning techniques have shown unexpected advancements that practically solve problems that never have been imagined to be automatized like face recognition or automated driving. However, food image recognition due to its high complexity and ambiguity, still remains far from being solved. In this project, we focus on how to combine two challenging research lines: deep learning and uncertainty modeling (epistemic and aleatoric uncertainty). After discussing our methodology to advance in this direction, we comment potential applications, as well as social and economic impact of the research on food image analysis.