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Electronics
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Artificial Intelligence in Image and Video Processing
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Artificial Intelligence in Image and Video Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 June 2024 | Viewed by 1991

Special Issue Editors

Dr. Yue Zhang

E-Mail Website
Guest Editor
School of Digital Media and Design Arts, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: object detection; multimodal learning; machine learning foundation model
Special Issues, Collections and Topics in MDPI journals
Dr. Bin Chen

E-Mail Website
Guest Editor
International Research Institute for Artificial Intelligence, Harbin Institute of Technology, Shenzhen 518055, China
Interests: Artificial Intelligence; object detection
Dr. Jinlin Guo

E-Mail
Guest Editor
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Interests: multimedia information retrieval; computer vision
Prof. Dr. Xueliang Liu

E-Mail Website
Guest Editor
School of Computing and Information Hefei University of Technology Hefei 230009, China
Interests: multimedia information retrieval; computer vision

Special Issue Information

Dear Colleagues,

With the popularity of various camera platforms, such as smart phones, new energy vehicles, satellites and drones, the number of images or videos being created has increased exponentially. The massive amounts of images or videos can be used in many different fields, and some typical tasks are proposed herein, including segmentation, classification, recognition, tracking, etc. For instance, image or video processing can be used for facial recognition, self-driving cars, geological survey, etc. However, it is still challenging to achieve high performance for artificial intelligence-based image and video interpretation in real-world scenarios. The reasons may be the complex noise, occlusion and deformation observed in these scenarios. Recently, advances in the machine learning computer vision domain have shown their potential in practical applications.

This Special Issue aims to publish novel ideas for artificial intelligence in image and video processing. In this Special Issue, original research articles and reviews are welcome. We request researchers, engineers and scientists to contribute their peer review research which explains research gaps including, but not limited to:

  • New architectures and theories for image and video processing;
  • New applications or tasks for image and video processing;
  • Fine-tuning and adaptation for large pretrained models;
  • Deep learning for smart phones images/videos;
  • Deep learning for surveillance images/videos;
  • Deep learning for remote sensing images/videos;
  • Deep learning for drone images/videos;
  • Artificial intelligence content generation and detection.

Dr. Yue Zhang
Dr. Bin Chen
Dr. Jinlin Guo
Prof. Dr. Xueliang Liu
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

  • computer vision
  • image analysis
  • video analysis
  • large pretrained model
  • surveillance images
  • remote sensing images
  • object detection
  • image segmentation
  • object tracking

Published Papers (2 papers)

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Research

15 pages, 672 KiB  
Article
Improving MLP-Based Weakly Supervised Crowd-Counting Network via Scale Reasoning and Ranking
by Ming Gao, Mingfang Deng, Huailin Zhao, Yangjian Chen and Yongqi Chen
Electronics 2024, 13(3), 471; https://doi.org/10.3390/electronics13030471 - 23 Jan 2024
Viewed by 636
Abstract
MLP-based weakly supervised crowd counting approaches have made significant advancements over the past few years. However, owing to the limited datasets, the current MLP-based methods do not consider the problem of region-to-region dependency in the image. For this, we propose a weakly supervised [...] Read more.
MLP-based weakly supervised crowd counting approaches have made significant advancements over the past few years. However, owing to the limited datasets, the current MLP-based methods do not consider the problem of region-to-region dependency in the image. For this, we propose a weakly supervised method termed SR2. SR2 consists of three parts: scale-reasoning module, scale-ranking module, and regression branch. In particular, the scale-reasoning module extracts and fuses the region-to-region dependency in the image and multiple scale feature, then sends the fused features to the regression branch to obtain estimated counts; the scale-ranking module is used to understand the internal information of the image better and expand the datasets efficiently, which will help to improve the accuracy of the estimated counts in the regression branch. We conducted extensive experiments on four benchmark datasets. The final results showed that our approach has better and higher competing counting performance with respect to other weakly supervised counting networks and with respect to some popular fully supervised counting networks. Full article
(This article belongs to the Special Issue Artificial Intelligence in Image and Video Processing)
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20 pages, 5283 KiB  
Article
Fault Classification and Diagnosis Approach Using FFT-CNN for FPGA-Based CORDIC Processor
by Yu Xie, He Chen, Yin Zhuang and Yizhuang Xie
Electronics 2024, 13(1), 72; https://doi.org/10.3390/electronics13010072 - 22 Dec 2023
Cited by 1 | Viewed by 710
Abstract
Within the realm of digital signal processing and communication systems, FPGA-based CORDIC (Coordinate Rotation Digital Computer) processors play pivotal roles, applied in trigonometric calculations and vector operations. However, soft errors have become one of the major threats in high-reliability FPGA-based applications, potentially degrading [...] Read more.
Within the realm of digital signal processing and communication systems, FPGA-based CORDIC (Coordinate Rotation Digital Computer) processors play pivotal roles, applied in trigonometric calculations and vector operations. However, soft errors have become one of the major threats in high-reliability FPGA-based applications, potentially degrading performance and causing system failures. This paper proposes a fault classification and diagnosis method for FPGA-based CORDIC processors, leveraging Fast Fourier Transform (FFT) and Convolutional Neural Networks (CNNs). The approach involves constructing fault classification datasets, optimizing features extraction through FFT to shorten the time of diagnosis and improve the diagnostic accuracy, and employing CNNs for training and testing of faults diagnosis. Different CNN architectures are tested to explore and construct the optimal fault classifier. Experimental results encompassing simulation and implementation demonstrate the improved accuracy and efficiency in fault classification and diagnosis. The proposed method provides fault prediction with an accuracy of more than 98.6% and holds the potential to enhance the reliability and performance of FPGA-based CORDIC circuit systems, surpassing traditional fault diagnosis methods such as Sum of Square (SoS). Full article
(This article belongs to the Special Issue Artificial Intelligence in Image and Video Processing)
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