Due to their extreme diversity, mutability and complexity, materials represent a particularly challenging class of visual stimuli, so understanding how we recognize materials, estimate their properties, predict their behaviour, and interact with them could give us more general insights into visual processing. What is ‘material appearance’, and how do we measure it and model it? How are material properties estimated and represented? Discussing these questions causes us to scrutinize the basic assumptions of ‘inverse optics’ that prevail in theories of human vision, and gives hints at how to build a machine vision system that could learn materials from observation.   

What You Will Learn:

• Basics of how material properties are physically described
• The challenges the brain faces in inferring material properties from retinal data
• Classic and recent findings material perception research
• New ideas in the application of unsupervised learning to material perception

Who Should Attend:

• The webinar is suitable for anyone interested in visual perception with at least an undergraduate level of training in psychology, neuroscience, computer science or related disciplines.

About the Presenter: Roland Fleming, Giessen University

Roland Fleming read PPP at Oxford, and did his PhD at MIT. After a postdoc at the Max Planck Institute for Biological Cybernetics, he joined Giessen University, where he is currently the Kurt Koffka Professor of Experimental Psychology. His research combines psychophysics, neural modelling, computer graphics and image analysis to understand how the brain estimates the physical properties of objects. He coordinated the EU-funded Marie Curie Training Network “PRISM: Perceptual Representation of Illumination, Shape and Materials”. In 2013 he was awarded the Young Investigator Award by the Vision Sciences Society, and in 2016 an ERC Consolidator Award for the project “SHAPE: On the perception of growth, form and process”.