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Open AccessArticle

A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications

1
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
2
School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
3
Department of Computing & Technology, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK
4
DICEAM, University Mediterranea of Reggio Calabria, 89124 Via Graziella Feo di Vito, Italy
*
Author to whom correspondence should be addressed.
Received: 15 August 2020 / Revised: 10 September 2020 / Accepted: 17 September 2020 / Published: 27 September 2020
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%. View Full-Text
Keywords: adaptive direction tracking filter; feature extraction; machine learning; shadow detection adaptive direction tracking filter; feature extraction; machine learning; shadow detection
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MDPI and ACS Style

Ju, Z.; Gun, L.; Hussain, A.; Mahmud, M.; Ieracitano, C. A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications. Appl. Sci. 2020, 10, 6761. https://0-doi-org.brum.beds.ac.uk/10.3390/app10196761

AMA Style

Ju Z, Gun L, Hussain A, Mahmud M, Ieracitano C. A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications. Applied Sciences. 2020; 10(19):6761. https://0-doi-org.brum.beds.ac.uk/10.3390/app10196761

Chicago/Turabian Style

Ju, Ziyi; Gun, Li; Hussain, Amir; Mahmud, Mufti; Ieracitano, Cosimo. 2020. "A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications" Appl. Sci. 10, no. 19: 6761. https://0-doi-org.brum.beds.ac.uk/10.3390/app10196761

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