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Title: Colour based semantic image segmentation and classification for unmanned ground operations
Authors: Coombes, Matthew
Eaton, William H.
Chen, Wen-Hua
Keywords: Unmanned ground operations
Image segmentation
Semantic segmentation
Colour classification
Bayesian network
Issue Date: 2016
Publisher: © IEEE
Citation: COOMBES, M., EATON, W.H. and CHEN, W.-H., 2016. Colour based semantic image segmentation and classification for unmanned ground operations. International Conference on Unmanned Aircraft Systems (ICUAS 2016), Arlington, VA USA, 7th-10th June 2016, pp. 858-867.
Abstract: To aid an automatic taxiing system for unmanned aircraft, this paper presents a colour based method for semantic segmentation and image classification in an aerodrome environment with the intention to use the classification output to aid navigation and collision avoidance. Based on previous work, this machine vision system uses semantic segmentation to interpret the scene. Following an initial superpixel based segmentation procedure, a colour based Bayesian Network classifier is trained and used to semantically classify each segmented cluster. HSV colourspace is adopted as it is close to the way of human vision perception of the world, and each channel shows significant differentiation between classes. Luminance is used to identify surface lines on the taxiway, which is then fused with colour classification to give improved classification results. The classification performance of the proposed colour based classifier is tested in a real aerodrome, which demonstrates that the proposed method outperforms a previously developed texture only based method.
Description: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor: This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) Autonomous and Intelligent Systems programme under the grant number EP/J011525/1 with BAE Systems as the leading industrial partner.
Version: Accepted for publication
DOI: 10.1109/ICUAS.2016.7502570
URI: https://dspace.lboro.ac.uk/2134/22729
Publisher Link: http://dx.doi.org/10.1109/ICUAS.2016.7502570
ISBN: 9781467393331
Appears in Collections:Conference Papers and Presentations (Aeronautical and Automotive Engineering)

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