Fine-grained Material Classification using Micro-geometry and Reflectance
File(s)
Accepted version
Author(s)
Kampouris, C
Zafeiriou, S
Ghosh, A
Malassiotis, S
Type
Conference Paper
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.
Date Issued
2016-09-16
Date Acceptance
2016-07-25
Citation
Lecture Notes in Computer Science, 2016, 9909, pp.778-792
ISSN
0302-9743
Publisher
Springer
Start Page
778
End Page
792
Journal / Book Title
Lecture Notes in Computer Science
Volume
9909
Copyright Statement
The final publication is available at Springer via https://dx.doi.org/10.1007/978-3-319-46454-1_47
Sponsor
The Royal Society
Engineering & Physical Science Research Council (EPSRC)
Grant Number
WM 120040
EP/N006259/1
Source
European Conference on Computer Vision 2016
Subjects
Artificial Intelligence & Image Processing
08 Information And Computing Sciences
Publication Status
Published
Start Date
2016-10-11
Finish Date
2016-10-14
Coverage Spatial
Amsterdam, the Netherlands