Next Article in Journal
How Long Should GPS Recording Lengths Be to Capture the Community Mobility of An Older Clinical Population? A Parkinson’s Example
Next Article in Special Issue
Multi-Modal Song Mood Detection with Deep Learning
Previous Article in Journal
A Comparative Study between Scanning Devices for 3D Printing of Personalized Ostomy Patches
Previous Article in Special Issue
Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks
Article

A Convolutional Neural Networks-Based Approach for Texture Directionality Detection

1
Institute of Electronics, Lodz University of Technology, Al. Politechniki 10, 93-590 Łódź, Poland
2
Department of Mechatronics, Faculty of Technical Science, University of Warmia and Mazury, Ul. Oczapowskiego 11, 10-710 Olsztyn, Poland
3
Information Technology Laboratory, Software and Systems Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Stefanos Kollias and Sylvain Girard
Sensors 2022, 22(2), 562; https://doi.org/10.3390/s22020562
Received: 8 November 2021 / Revised: 4 January 2022 / Accepted: 7 January 2022 / Published: 12 January 2022
The perceived texture directionality is an important, not fully explored image characteristic. In many applications texture directionality detection is of fundamental importance. Several approaches have been proposed, such as the fast Fourier-based method. We recently proposed a method based on the interpolated grey-level co-occurrence matrix (iGLCM), robust to image blur and noise but slower than the Fourier-based method. Here we test the applicability of convolutional neural networks (CNNs) to texture directionality detection. To obtain the large amount of training data required, we built a training dataset consisting of synthetic textures with known directionality and varying perturbation levels. Subsequently, we defined and tested shallow and deep CNN architectures. We present the test results focusing on the CNN architectures and their robustness with respect to image perturbations. We identify the best performing CNN architecture, and compare it with the iGLCM, the Fourier and the local gradient orientation methods. We find that the accuracy of CNN is lower, yet comparable to the iGLCM, and it outperforms the other two methods. As expected, the CNN method shows the highest computing speed. Finally, we demonstrate the best performing CNN on real-life images. Visual analysis suggests that the learned patterns generalize to real-life image data. Hence, CNNs represent a promising approach for texture directionality detection, warranting further investigation. View Full-Text
Keywords: directionality detection; texture; convolutional neural networks directionality detection; texture; convolutional neural networks
Show Figures

Figure 1

MDPI and ACS Style

Kociołek, M.; Kozłowski, M.; Cardone, A. A Convolutional Neural Networks-Based Approach for Texture Directionality Detection. Sensors 2022, 22, 562. https://doi.org/10.3390/s22020562

AMA Style

Kociołek M, Kozłowski M, Cardone A. A Convolutional Neural Networks-Based Approach for Texture Directionality Detection. Sensors. 2022; 22(2):562. https://doi.org/10.3390/s22020562

Chicago/Turabian Style

Kociołek, Marcin, Michał Kozłowski, and Antonio Cardone. 2022. "A Convolutional Neural Networks-Based Approach for Texture Directionality Detection" Sensors 22, no. 2: 562. https://doi.org/10.3390/s22020562

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop