Abstract
The images of different road terrain types present abundant information and significantly different characteristics. Hence, it is considered to use a camera to capture images of road surfaces in an attempt to overcome the issues found using accelerometer data only. By processing and extracting features from those images, road terrain classification is expected to be improved.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brodatz P (1966) Texture: a photographic album for artists and designers. Dover Publications, New York, USA
Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3(6): 610–621
Soh L, Tsatsoulis C (1999) Texture analysis of SAR sea ice imagery using grey level co-occurrence matrices. IEEE Trans Geosci Remote Sens 37(2):780–795
Clausi D (2002) An analysis of co-occurrence texture statistics as a function of grey level quantization. Remote Sens 28(1):45–62
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 China Machine Press, Beijing and Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Wang, S. (2019). Image-Based Road Terrain Classification. In: Road Terrain Classification Technology for Autonomous Vehicle. Unmanned System Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-13-6155-5_4
Download citation
DOI: https://doi.org/10.1007/978-981-13-6155-5_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6154-8
Online ISBN: 978-981-13-6155-5
eBook Packages: EngineeringEngineering (R0)