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2016 | OriginalPaper | Chapter

Fine-Grained Material Classification Using Micro-geometry and Reflectance

Authors : Christos Kampouris, Stefanos Zafeiriou, Abhijeet Ghosh, Sotiris Malassiotis

Published in: Computer Vision – ECCV 2016

Publisher: Springer International Publishing

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Abstract

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.

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We have tried other features such as Local Binary Patterns (LBPs) [39] and the MR8 filters in [2] but the results were much poorer than dense SIFT, hence are not reported.

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Title: Fine-Grained Material Classification Using Micro-geometry and Reflectance
Authors: Christos Kampouris
Stefanos Zafeiriou
Abhijeet Ghosh
Sotiris Malassiotis
Publisher: Springer International Publishing
Book: Computer Vision – ECCV 2016
Print ISBN: 978-3-319-46453-4

Electronic ISBN: 978-3-319-46454-1

Copyright Year: 2016
DOI: https://doi.org/10.1007/978-3-319-46454-1_47

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