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Sensors Technologies for Sound and Image Processing
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Sensors Technologies for Sound and Image Processing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 5240

Special Issue Editor

Dr. Xiaobin Zhu

E-Mail Website
Guest Editor
Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing, China
Interests: machine learning; multimedia analysis; intelligent analysis of images and videos

Special Issue Information

Dear Colleagues,

We invite you to submit your work to this Special Issue on sensor technologies for sound and image processing, which have attracted much attention in recent years. There is an urgent need for the development of new technologies, models, and algorithms to solve sound and image processing challenges in the field of sensors. This Special Issue aims to highlight new ideas, original trend analyses, originally developed software, new methods, and other research concerning sensors, signal processing and computer vision, with a view to uniting research communities interested in sensors, signal processing and computer vision.

This Special Issue will focus on a broad range of sensors as well as sound and image processing, and will involve the introduction and development of new advanced theoretical and practical algorithms. Potential topics include, but are not limited to:

  • Image processing and recognition;
  • Sound processing and recognition;
  • Automatic speech recognition;
  • Novel image descriptors;
  • Texture analysis and shape recognition;
  • Feature extraction and image registration;
  • Image understanding and reconstruction;
  • Pattern recognition and analysis;
  • Biomedical signal processing;
  • Multi-objective signal processing optimization;
  • Wearable sensors.

Dr. Xiaobin Zhu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sound recognition
  • image processing
  • signal processing
  • pattern recognition

Published Papers (2 papers)

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Research

14 pages, 1089 KiB  
Article
Faster SCDNet: Real-Time Semantic Segmentation Network with Split Connection and Flexible Dilated Convolution
by Shu Tian, Guangyu Yao and Songlu Chen
Sensors 2023, 23(6), 3112; https://doi.org/10.3390/s23063112 - 14 Mar 2023
Cited by 1 | Viewed by 1323
Abstract
Recently, semantic segmentation has been widely applied in various realistic scenarios. Many semantic segmentation backbone networks use various forms of dense connection to improve the efficiency of gradient propagation in the network. They achieve excellent segmentation accuracy but lack inference speed. Therefore, we [...] Read more.
Recently, semantic segmentation has been widely applied in various realistic scenarios. Many semantic segmentation backbone networks use various forms of dense connection to improve the efficiency of gradient propagation in the network. They achieve excellent segmentation accuracy but lack inference speed. Therefore, we propose a backbone network SCDNet with a dual path structure and higher speed and accuracy. Firstly, we propose a split connection structure, which is a streamlined lightweight backbone with a parallel structure to increase inference speed. Secondly, we introduce a flexible dilated convolution using different dilation rates so that the network can have richer receptive fields to perceive objects. Then, we propose a three-level hierarchical module to effectively balance the feature maps with multiple resolutions. Finally, a refined flexible and lightweight decoder is utilized. Our work achieves a trade-off of accuracy and speed on the Cityscapes and Camvid datasets. Specifically, we obtain a 36% improvement in FPS and a 0.7% improvement in mIoU on the Cityscapes test set. Full article
(This article belongs to the Special Issue Sensors Technologies for Sound and Image Processing)
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18 pages, 7913 KiB  
Article
Global–Local Facial Fusion Based GAN Generated Fake Face Detection
by Ziyu Xue, Xiuhua Jiang, Qingtong Liu and Zhaoshan Wei
Sensors 2023, 23(2), 616; https://doi.org/10.3390/s23020616 - 05 Jan 2023
Cited by 2 | Viewed by 3481
Abstract
Media content forgery is widely spread over the Internet and has raised severe societal concerns. With the development of deep learning, new technologies such as generative adversarial networks (GANs) and media forgery technology have already been utilized for politicians and celebrity forgery, which [...] Read more.
Media content forgery is widely spread over the Internet and has raised severe societal concerns. With the development of deep learning, new technologies such as generative adversarial networks (GANs) and media forgery technology have already been utilized for politicians and celebrity forgery, which has a terrible impact on society. Existing GAN-generated face detection approaches rely on detecting image artifacts and the generated traces. However, these methods are model-specific, and the performance is deteriorated when faced with more complicated methods. What’s more, it is challenging to identify forgery images with perturbations such as JPEG compression, gamma correction, and other disturbances. In this paper, we propose a global–local facial fusion network, namely GLFNet, to fully exploit the local physiological and global receptive features. Specifically, GLFNet consists of two branches, i.e., the local region detection branch and the global detection branch. The former branch detects the forged traces from the facial parts, such as the iris and pupils. The latter branch adopts a residual connection to distinguish real images from fake ones. GLFNet obtains forged traces through various ways by combining physiological characteristics with deep learning. The method is stable with physiological properties when learning the deep learning features. As a result, it is more robust than the single-class detection methods. Experimental results on two benchmarks have demonstrated superiority and generalization compared with other methods. Full article
(This article belongs to the Special Issue Sensors Technologies for Sound and Image Processing)
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