Multi-spectral material classification in landscape scenes using commodity hardware
Bradbury G, Mitchell K, Weyrich T (2013)
Publication Type: Conference contribution
Publication year: 2013
Journal
Book Volume: 8048 LNCS
Pages Range: 209-216
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: GBR
ISBN: 9783642402456
DOI: 10.1007/978-3-642-40246-3_26
Abstract
We investigate the advantages of a stereo, multi-spectral acquisition system for material classification in ground-level landscape images. Our novel system allows us to acquire high-resolution, multi-spectral stereo pairs using commodity photographic equipment. Given additional spectral information we obtain better classification of vegetation classes than the standard RGB case. We test the system in two modes: splitting the visible spectrum into six bands; and extending the recorded spectrum to near infra-red. Our six-band design is more practical than standard multi-spectral techniques and foliage classification using acquired images compares favourably to using a standard camera. © 2013 Springer-Verlag.
Authors with CRIS profile
Involved external institutions
University College London (UCL)
United Kingdom (GB)
The Walt Disney Company
United States (USA) (US)
United Kingdom (GB)
The Walt Disney Company
United States (USA) (US)
How to cite
APA:
Bradbury, G., Mitchell, K., & Weyrich, T. (2013). Multi-spectral material classification in landscape scenes using commodity hardware. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 209-216). GBR.
MLA:
Bradbury, Gwyneth, Kenny Mitchell, and Tim Weyrich. "Multi-spectral material classification in landscape scenes using commodity hardware." Proceedings of the 15th International Conference on Computer Analysis of Images and Patterns, CAIP 2013, GBR 2013. 209-216.
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