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Keynote Lecture

 

Uncertainty Modeling within an End-to-end Framework for Food Image Analysis

Petia Radeva
Universitat de Barcelona
Spain
 

Brief Bio
Prof. Petia Radeva completed her undergraduate study on Applied Mathematics and Informatics at the University of Sofia, Bulgaria, in 1989. In 1996, she received a Ph.D. degree in Computer Vision at UAB. In 2007, she moved as Tenured Associate professor at the Universitat de Barcelona (UB), Department of Mathematics and Informatics, where from 2009 to 2013 she was Director of Computer Science Undergraduate Studies. In 2018, Petia Radeva became Full professor at the Universitat de Barcelona. Petia Radeva is Head of the Consolidated Group Computer Vision at the University of Barcelona (CVUB) at UB (www.ub.edu/cvub) and Head of the Medical Imaging Laboratory of Computer Vision Center (www.cvc.uab.es). Petia Radeva’s research interests are on Development of learning-based approaches (especially, deep learning) for computer vision, and their application to health. Currently, she is involved on projects that study the application of wearable cameras and life-logging, to extract visual diary of individuals to be used for memory reinforcement of patients with mental diseases (e.g. Mild cognitive impairment). Moreover, she is exploring how to extract semantically meaningful events that characterize lifestyle and healthy habits of people from egocentric data. Petia Radeva is Principal Investigator of the Universitat de Barcelona in two European projects devoted to food intake monitoring for patients with kidney transplants and for older people. There her team applies most recent and advanced methods for food image analysis using Deep learning models. Petia Radeva is REA-FET-OPEN vice-chair from 2015 and has actively participated from 2018 as a mentor in the Wild Cards EIT program. Petia Radeva is associate editor of Pattern Recognition journal and International Journal of Visual Communication and Image Representation. She obtained the ICREA award from the Catalonian Government for her scientific merits in 2014, the international award “Aurora Pons Porrata” from CIARP in 2016 and the Prize “Antonio Caparrós” for the best technology transfer project of 2013.


Abstract
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) in order to address the food image recognition. We will show the relevance of food ontology and GANs within this research line. 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.



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