Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.6
Journals
Electronics
Special Issues
Recent Advancements in Signal and Vision Analysis
share announcement

Recent Advancements in Signal and Vision Analysis

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 2752

Special Issue Editors

Dr. Jianji Wang

E-Mail Website
Guest Editor
Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, China
Interests: computer vision; data analysis; signal processing; machine learning
Dr. Meng Yang

E-Mail Website
Guest Editor
Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, China
Interests: machine vision; intelligent systems; multi-modal vision; signal processing

Special Issue Information

Dear Colleagues,

Signal and vision analysis refers to the interdisciplinary domain of research that focuses on extracting meaningful information from signals and images. The field has undergone a remarkable transformation in recent years, fueled by the convergence of powerful computing technologies, sophisticated algorithms, and the increasing availability of visual and signal data.    The Special Issue "Recent Advancements in signal and vision analysis" aims to explore the latest developments in the fields of signal and vision analysis. This Special Issue provides a platform for researchers, scholars, and practitioners to share their cutting-edge research findings, insights, and innovations within the context of signal processing and computer vision. The scope of this Special Issue includes, but is not limited to:

  • Image Processing and Computer Vision: Cutting-edge techniques in image enhancement, feature extraction, object recognition, and computer vision for various applications.
  • Signal Analysis and Machine Learning: Innovations in signal analysis, pattern recognition, and the integration of machine learning and deep learning in signal and image analysis.
  • Medical Imaging and Healthcare Applications: Advancements in medical imaging, healthcare diagnostics, and the impact of signal and vision analysis on patient care.
  • Security and Surveillance Systems: State-of-the-art developments in security and surveillance, including facial recognition, video analysis, and threat detection.
  • Augmented and Virtual Reality: How signal and vision analysis are shaping immersive experiences, augmented reality, virtual reality, and interactive simulations.
  • Industrial Automation: Signal analysis is integral to quality control, predictive maintenance, and process optimization in manufacturing and industrial settings.

We look forward to receiving your contributions.

Dr. Jianji Wang
Dr. Meng Yang
Guest Editors

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. Electronics 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 2400 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

  • image processing and computer vision
  • signal analysis and machine learning
  • medical imaging and healthcare applications
  • security and surveillance systems
  • augmented and virtual reality
  • industrial automation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 3683 KiB  
Article
Depth Image Rectification Based on an Effective RGB–Depth Boundary Inconsistency Model
by Hao Cao, Xin Zhao, Ang Li and Meng Yang
Electronics 2024, 13(16), 3330; https://doi.org/10.3390/electronics13163330 - 22 Aug 2024
Viewed by 697
Abstract
Depth image has been widely involved in various tasks of 3D systems with the advancement of depth acquisition sensors in recent years. Depth images suffer from serious distortions near object boundaries due to the limitations of depth sensors or estimation methods. In this [...] Read more.
Depth image has been widely involved in various tasks of 3D systems with the advancement of depth acquisition sensors in recent years. Depth images suffer from serious distortions near object boundaries due to the limitations of depth sensors or estimation methods. In this paper, a simple method is proposed to rectify the erroneous object boundaries of depth images with the guidance of reference RGB images. First, an RGB–Depth boundary inconsistency model is developed to measure whether collocated pixels in depth and RGB images belong to the same object. The model extracts the structures of RGB and depth images, respectively, by Gaussian functions. The inconsistency of two collocated pixels is then statistically determined inside large-sized local windows. In this way, pixels near object boundaries of depth images are identified to be erroneous when they are inconsistent with collocated ones in RGB images. Second, a depth image rectification method is proposed by embedding the model into a simple weighted mean filter (WMF). Experiment results on two datasets verify that the proposed method well improves the RMSE and SSIM of depth images by 2.556 and 0.028, respectively, compared with recent optimization-based and learning-based methods. Full article
(This article belongs to the Special Issue Recent Advancements in Signal and Vision Analysis)
►▼ Show Figures

Figure 1

27 pages, 6797 KiB  
Article
Constrained Spectral–Spatial Attention Residual Network and New Cross-Scene Dataset for Hyperspectral Classification
by Siyuan Li, Baocheng Chen, Nan Wang, Yuetian Shi, Geng Zhang and Jia Liu
Electronics 2024, 13(13), 2540; https://doi.org/10.3390/electronics13132540 - 28 Jun 2024
Viewed by 755
Abstract
Hyperspectral image classification is widely applied in several fields. Since existing datasets focus on a single scene, current deep learning-based methods typically divide patches randomly on the same image as training and testing samples. This can result in similar spatial distributions of samples, [...] Read more.
Hyperspectral image classification is widely applied in several fields. Since existing datasets focus on a single scene, current deep learning-based methods typically divide patches randomly on the same image as training and testing samples. This can result in similar spatial distributions of samples, which may incline the network to learn specific spatial distributions in pursuit of falsely high accuracy. In addition, the large variation between single-scene datasets has led to research in cross-scene hyperspectral classification, focusing on domain adaptation and domain generalization while neglecting the exploration of the generalizability of models to specific variables. This paper proposes two approaches to address these issues. The first approach is to train the model on the original image and then test it on the rotated dataset to simulate cross-scene evaluation. The second approach is constructing a new cross-scene dataset for spatial distribution variations, named GF14-C17&C16, to avoid the problems arising from the existing single-scene datasets. The image conditions in this dataset are basically the same, and only the land cover distribution is different. In response to the spatial distribution variations, this paper proposes a constrained spectral attention mechanism and a constrained spatial attention mechanism to limit the fitting of the model to specific feature distributions. Based on these, this paper also constructs a constrained spectral–spatial attention residual network (CSSARN). Extensive experimental results on two public hyperspectral datasets and the GF14-C17&C16 dataset have demonstrated that CSSARN is more effective than other methods in extracting cross-scene spectral and spatial features. Full article
(This article belongs to the Special Issue Recent Advancements in Signal and Vision Analysis)
►▼ Show Figures

Figure 1

19 pages, 2438 KiB  
Article
Secrecy and Throughput Performance of Cooperative Cognitive Decode-and-Forward Relaying Vehicular Networks with Direct Links and Poisson Distributed Eavesdroppers
by Fan Wang, Cuiran Li, Jianli Xie, Lin Su, Yadan Liu and Shaoyi Du
Electronics 2024, 13(4), 777; https://doi.org/10.3390/electronics13040777 - 16 Feb 2024
Viewed by 882
Abstract
Cooperative communication and cognitive radio can effectively improve spectrum utilization, coverage range, and system throughput of vehicular networks, whereas they also incur several security issues and wiretapping attacks. Thus, security and threat detection are vitally important for such networks. This paper investigates the [...] Read more.
Cooperative communication and cognitive radio can effectively improve spectrum utilization, coverage range, and system throughput of vehicular networks, whereas they also incur several security issues and wiretapping attacks. Thus, security and threat detection are vitally important for such networks. This paper investigates the secrecy and throughput performance of an underlay cooperative cognitive vehicular network, where a pair of secondary vehicles communicate through a direct link and the assistance of a decode-and-forward (DF) secondary relay in the presence of Poisson-distributed colluding eavesdroppers and under an interference constraint set by the primary receiver. Considering mixed Rayleigh and double-Rayleigh fading channels, we design a realistic relaying transmission scheme and derive the closed-form expressions of secrecy and throughput performance, such as the secrecy outage probability (SOP), the connection outage probability (COP), the secrecy and connection outage probability (SCOP), and the overall secrecy throughput, for traditional and proposed schemes, respectively. An asymptotic analysis is further presented in the high signal-to-noise ratio (SNR) regime. Numerical results illustrate the impacts of network parameters on secrecy and throughput and reveal that the advantages of the proposed scheme are closely related to the channel gain of the relay link compared to the direct link. Full article
(This article belongs to the Special Issue Recent Advancements in Signal and Vision Analysis)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop