The Czech Science Foundation Grant GA22-17529S (2022-2024)
Visual Fingerprint of Material Appearance
Beneficiary: Institute of Information Theory and Automation (UTIA), The Czech Academy of Sciences
Abstract
Real-world comprises thousands of materials having different surface appearances. These appearances are a key in our everyday judgements of material properties, which are based on our past experience, recognition, and usage of these materials.
Although current technology allows for realistic reproduction of material appearance for visualization and quality control purposes, sharing of materials and their properties information across different measured representations and software platforms is still rather complicated. This problem relates to digital material appearance assessment over entire pipeline of its lifetime, i.e. from its acquisition, measured data modeling, to its visualization.
The goal of this project is to create material identifier encoding its perceptual visual features. These features obtained by judgements of observers in psychophysical studies form so called material fingerprint. Based on sparse material measurements and the qualities judgements, we plan to provide their generalization and propagation in a form of ontology of existing material appearances based on methods of adaptive machine learning. Such a system would assess regular measurements of any material appearance and provide its perceptual fingerprint allowing its efficient categorization, retrieval and sharing among users.
Although current technology allows for realistic reproduction of material appearance for visualization and quality control purposes, sharing of materials and their properties information across different measured representations and software platforms is still rather complicated. This problem relates to digital material appearance assessment over entire pipeline of its lifetime, i.e. from its acquisition, measured data modeling, to its visualization.
The goal of this project is to create material identifier encoding its perceptual visual features. These features obtained by judgements of observers in psychophysical studies form so called material fingerprint. Based on sparse material measurements and the qualities judgements, we plan to provide their generalization and propagation in a form of ontology of existing material appearances based on methods of adaptive machine learning. Such a system would assess regular measurements of any material appearance and provide its perceptual fingerprint allowing its efficient categorization, retrieval and sharing among users.
Participants
| Jiri Filip - (principal investigator), Institute of Information Theory and Automation (UTIA), The Czech Academy of Sciences. He focuses on measurement and analysis of material appearance and on development of novel measurement approaches. | |
| Jiri Lukavsky - Institute of Psychology (PSU) The Czech Academy of Sciences. Experienced researcher focused on visual perception, attention and memory, and its mathematical modelling. | |
| Roland Fleming - Department of Psychology, Giessen University. His research combines psychophysics, neural modelling, computer graphics and image analysis to understand how the brain estimates the physical properties of objects. | |
| Petr Somol - Institute of Information Theory and Automation (UTIA), The Czech Academy of Sciences / AI Research director, Avast. He is a leading expert in a field of feature selection and data mining with rich research and industrial experience. | |
| Radomir Vavra - Institute of Information Theory and Automation (UTIA), The Czech Academy of Sciences. An expert with extensive experience with material appearance measurement (BRDF, BTF) and its visualization. | |
