A novel approach to shadow boundary detection based on an adaptive direction-tracking filter for brain-machine interface applications

Ju, Z, Gun, L, Hussain, A, Mahmud, M ORCID: https://orcid.org/0000-0002-2037-8348 and Ieracitano, C, 2020. A novel approach to shadow boundary detection based on an adaptive direction-tracking filter for brain-machine interface applications. Applied Sciences, 10 (19): 6761. ISSN 2076-3417

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Abstract

In this paper, a Brain-Machine Interface (BMI) system is proposed to automatically control the navigation of wheelchairs by detecting the shadows on their route. In this context, a new algorithm to detect shadows in a single image is proposed. Specifically, a novel adaptive direction tracking filter (ADT) is developed to extract feature information along the direction of shadow boundaries. The proposed algorithm avoids extraction of features around all directions of pixels, which significantly improves the efficiency and accuracy of shadow features extraction. Higher-order statistics (HOS) features such as skewness and kurtosis in addition to other optical features are used as input to different Machine Learning (ML) based classifiers, specifically, a Multilayer Perceptron (MLP), Autoencoder (AE), 1D-Convolutional Neural Network (1D-CNN) and Support Vector Machine (SVM), to perform the shadow boundaries detection task. Comparative results demonstrate that the proposed MLP-based system outperforms all the other state-of-the-art approaches, reporting accuracy rates up to 84.63%.

Item Type:	Journal article
Publication Title:	Applied Sciences
Creators:	Ju, Z., Gun, L., Hussain, A., Mahmud, M. and Ieracitano, C.
Publisher:	MDPI AG
Date:	1 October 2020
Volume:	10
Number:	19
ISSN:	2076-3417
Identifiers:	Number Type 10.3390/app10196761 DOI 1370281 Other
Rights:	© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Jill Tomkinson
Date Added:	29 Sep 2020 13:49
Last Modified:	31 May 2021 15:16
URI:	https://irep.ntu.ac.uk/id/eprint/40997

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