Imperial College London
Alternate

STEFANOS ZAFEIRIOU, PhD

Faculty of Engineering, Department of Computing

Professor in Machine Learning & Computer Vision
 
 
 
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Contact

 

+44 (0)20 7594 8461s.zafeiriou Website CV

 
 
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Location

 

375Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Kampouris:2016:10.1007/978-3-319-46454-1_47,
author = {Kampouris, C and Zafeiriou, S and Ghosh, A and Malassiotis, S},
doi = {10.1007/978-3-319-46454-1_47},
pages = {778--792},
publisher = {Springer},
title = {Fine-grained Material Classification using Micro-geometry and Reflectance},
url = {http://dx.doi.org/10.1007/978-3-319-46454-1_47},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - In this paper we focus on an understudied computer vision problem, particularly how the micro-geometry and the reflectance of a surface can be used to infer its material. To this end, we introduce a new, publicly available database for fine-grained material classification, consisting of over 2000 surfaces of fabrics (http://ibug.doc.ic.ac.uk/resources/fabrics.). The database has been collected using a custom-made portable but cheap and easy to assemble photometric stereo sensor. We use the normal map and the albedo of each surface to recognize its material via the use of handcrafted and learned features and various feature encodings. We also perform garment classification using the same approach. We show that the fusion of normals and albedo information outperforms standard methods which rely only on the use of texture information. Our methodologies, both for data collection, as well as for material classification can be applied easily to many real-word scenarios including design of new robots able to sense materials and industrial inspection.
AU  - Kampouris,C
AU  - Zafeiriou,S
AU  - Ghosh,A
AU  - Malassiotis,S
DO  - 10.1007/978-3-319-46454-1_47
EP  - 792
PB  - Springer
PY  - 2016///
SN  - 0302-9743
SP  - 778
TI  - Fine-grained Material Classification using Micro-geometry and Reflectance
UR  - http://dx.doi.org/10.1007/978-3-319-46454-1_47
UR  - http://hdl.handle.net/10044/1/41053
ER  -
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