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Electronics
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Research Advances in Image Processing and Computer Vision
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Research Advances in Image Processing and Computer Vision

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

Deadline for manuscript submissions: closed (15 January 2025) | Viewed by 8217

Special Issue Editors

Dr. Yan Wang

E-Mail Website
Guest Editor
School of Computer Science, Sichuan University, Chengdu 610000, China
Interests: computer vision; machine learning; deep learning; medical image processing
Prof. Dr. Tao Zhou

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: computer vision; machine learning; medical image analysis; AI in healthcare
Special Issues, Collections and Topics in MDPI journals
Dr. Xu Yang

E-Mail Website
Guest Editor
Institute of Automation, Chinese Academy of Sciences, Beijing 100000, China
Interests: computer/robot vision; pattern analysis; robot cognition and developmental learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image processing and computer vision are two important and interconnected fields of artificial intelligence that have a wide range of applications in various domains, such as medicine, security, entertainment, education, etc. With the rapid development of machine learning and deep learning techniques, image processing and computer vision have achieved significant advances and breakthroughs in recent years. However, there are still many challenges and open problems that need to be addressed by researchers and practitioners, such as dealing with noisy, incomplete, or heterogeneous data, improving the robustness, efficiency, and interpretability of the algorithms, bridging the gap between low-level and high-level vision tasks, integrating prior knowledge and domain expertise, etc.

The objective of this Special Issue is to offer a stimulating platform for researchers to present and examine their latest research outcomes while sharing their valuable insights on the present and future directions of image processing and computer vision. This is an open invitation for original and high-quality contributions, including theoretical, methodological, and practical work related to image processing and computer vision and that encompasses their applications across different domains. Survey papers that provide a comprehensive overview of the recent progress and trends in this interdisciplinary field will also be warmly received.

Topics of interest include, but are not limited to, the following:

  • Image analysis, understanding, retrieval, and annotation;
  • Image generation, synthesis, denoising and super-resolution;
  • Object recognition and segmentation;
  • Face detection, recognition, verification, and emotion analysis;
  • Machine learning and deep learning methods for image processing and computer vision;
  • Explainable and interpretable image processing and computer vision models;
  • Transfer learning, domain adaptation, and multi-task learning for image processing and computer vision;
  • Image processing and computer vision for various applications, e.g., medical field, computer security, etc.

Dr. Yan Wang
Prof. Dr. Tao Zhou
Dr. Xu 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
  • computer vision
  • machine learning
  • deep learning
  • artificial intelligence

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Published Papers (4 papers)

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13 pages, 2873 KiB  
Article
Deep Multi-Instance Conv-Transformer Frameworks for Landmark-Based Brain MRI Classification
by Guannan Li, Zexuan Ji and Quansen Sun
Electronics 2024, 13(5), 980; https://doi.org/10.3390/electronics13050980 - 4 Mar 2024
Cited by 4 | Viewed by 1833
Abstract
For brain diseases, e.g., autism spectrum disorder (ASD), with unclear biological characteristics, the detection of imaging-based biomarkers is a critical task for diagnosis. Several landmark-based categorization approaches have been developed for the computer-aided diagnosis of brain diseases, such as Alzheimer’s disease (AD), utilizing [...] Read more.
For brain diseases, e.g., autism spectrum disorder (ASD), with unclear biological characteristics, the detection of imaging-based biomarkers is a critical task for diagnosis. Several landmark-based categorization approaches have been developed for the computer-aided diagnosis of brain diseases, such as Alzheimer’s disease (AD), utilizing structural magnetic resonance imaging (sMRI). With the automatic detection of the landmarks of brain disease, more detailed brain features were identified for clinical diagnosis. Multi-instance learning is an effective technique for classifying brain diseases based on landmarks. The multiple-instance learning approach relies on the assumption of independent distribution hypotheses and is mostly focused on local information, thus the correlation among different brain regions may be ignored. However, according to previous research on ASD and AD, the abnormal development of different brain regions is highly correlated. Vision Transformers, with self-attention modules to capture the relationship between embedded patches from a whole image, have recently demonstrated superior performances in many computer vision tasks. Nevertheless, the utilization of 3D brain MRIs imposes a substantial computational load, especially while training with Vision Transformer. To address the challenges mentioned above, in this research, we proposed a landmark-based multi-instance Conv-Transformer (LD-MILCT) framework as a solution to the aforementioned issues in brain disease diagnosis. In this network, a two-stage multi-instance learning strategy was proposed to explore both spatial and morphological information between different brain regions; the Vision Transformer utilizes a multi-instance learning head (MIL head) to fully utilize the features that are not involved in the ultimate classification. We assessed our proposed framework using T1-weighted MRI images from both AD and ASD databases. Our method outperformed existing deep learning and landmark-based methods in terms of brain MRI classification tasks. Full article
(This article belongs to the Special Issue Research Advances in Image Processing and Computer Vision)
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21 pages, 4575 KiB  
Article
AGProto: Adaptive Graph ProtoNet towards Sample Adaption for Few-Shot Malware Classification
by Junbo Wang, Tongcan Lin, Huyu Wu and Peng Wang
Electronics 2024, 13(5), 935; https://doi.org/10.3390/electronics13050935 - 29 Feb 2024
Cited by 1 | Viewed by 1391
Abstract
Traditional malware-classification methods reliant on large pre-labeled datasets falter when encountering new or evolving malware types, particularly when only a few samples are available. And most current models utilize a fixed architecture; however, the characteristics of the various types of malware differ significantly. [...] Read more.
Traditional malware-classification methods reliant on large pre-labeled datasets falter when encountering new or evolving malware types, particularly when only a few samples are available. And most current models utilize a fixed architecture; however, the characteristics of the various types of malware differ significantly. This discrepancy results in notably inferior classification performance for certain categories or samples with uncommon features, but the threats of these malware samples are of equivalent significance. In this paper, we introduce Adaptive Graph ProtoNet (AGProto), a novel approach for classifying malware in the field of Few-Shot Learning. AGProto leverages Graph Neural Networks (GNNs) to propagate sample features and generate multiple prototypes. It employs an attention mechanism to calculate the relevance of each prototype to individual samples, resulting in a customized prototype for each case. Our approach achieved optimal performance on two few-shot malware classification datasets, surpassing other competitive models with an accuracy improvement of over 2%. In extremely challenging scenarios—specifically, 20-class classification tasks with only five samples per class—our method notably excelled, achieving over 70% accuracy, significantly outperforming existing advanced techniques. Full article
(This article belongs to the Special Issue Research Advances in Image Processing and Computer Vision)
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17 pages, 6457 KiB  
Article
Oriented Object Detection in Remote Sensing Using an Enhanced Feature Pyramid Network
by Xinyu Zhu, Wei Zhou, Kun Wang, Bing He, Ying Fu, Xi Wu and Jiliu Zhou
Electronics 2023, 12(17), 3559; https://doi.org/10.3390/electronics12173559 - 23 Aug 2023
Viewed by 1445
Abstract
Object detection in remote sensing images is a critical task within the field of remote sensing image interpretation and analysis, serving as a fundamental foundation for military surveillance and traffic guidance. Recently, although many object detection algorithms have been improved to adapt to [...] Read more.
Object detection in remote sensing images is a critical task within the field of remote sensing image interpretation and analysis, serving as a fundamental foundation for military surveillance and traffic guidance. Recently, although many object detection algorithms have been improved to adapt to the characteristics of remote sensing images and have achieved good performance, most of them still use horizontal bounding boxes, which struggle to accurately mark targets with multiple angles and dense arrangements in remote sensing images. We propose an oriented bounding box optical remote sensing image object detection method based on an enhanced feature pyramid, and add an attention module to suppress background noise. To begin with, we incorporate an angle prediction module that accurately locates the detection target. Subsequently, we design an enhanced feature pyramid network, utilizing deformable convolutions and feature fusion modules to enhance the feature information of rotating targets and improve the expressive capacity of features at all levels. The proposed algorithm in this paper performs well on the public DOTA dataset and HRSC2016 dataset, compared with other object detection methods, and the detection accuracy AP values of most object categories are improved by at least three percentage points. The results show that our method can accurately locate densely arranged and dynamically oriented targets, significantly reducing the risk of missing detections, and achieving higher levels of target detection accuracy. Full article
(This article belongs to the Special Issue Research Advances in Image Processing and Computer Vision)
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15 pages, 14034 KiB  
Article
HPG-GAN: High-Quality Prior-Guided Blind Face Restoration Generative Adversarial Network
by Xu Deng, Hao Zhang and Xiaojie Li
Electronics 2023, 12(16), 3418; https://doi.org/10.3390/electronics12163418 - 11 Aug 2023
Cited by 2 | Viewed by 2294
Abstract
To address the problems of low resolution, compression artifacts, complex noise, and color loss in image restoration, we propose a High-Quality Prior-Guided Blind Face Restoration Generative Adversarial Network (HPG-GAN). This mainly consists of Coarse Restoration Sub-Network (CR-Net) and Fine Restoration Sub-Network (FR-Net). HPG-GAN [...] Read more.
To address the problems of low resolution, compression artifacts, complex noise, and color loss in image restoration, we propose a High-Quality Prior-Guided Blind Face Restoration Generative Adversarial Network (HPG-GAN). This mainly consists of Coarse Restoration Sub-Network (CR-Net) and Fine Restoration Sub-Network (FR-Net). HPG-GAN extracts high-quality structural and textural priors and facial feature priors from coarse restoration images to reconstruct clear and high-quality facial images. FR-Net includes the Facial Feature Enhancement Module (FFEM) and the Asymmetric Feature Fusion Module (AFFM). FFEM enhances facial feature information using high-definition facial feature priors obtained from ArcFace. AFFM fuses and selects asymmetric high-quality structural and textural information from ResNet34 to recover overall structural and textural information. The comparative evaluations on synthetic and real-world datasets demonstrate superior performance and visual restoration effects compared to state-of-the-art methods. The ablation experiments validate the importance of each module. HPG-GAN is an effective and robust blind face deblurring and restoration network. The experimental results demonstrate the effectiveness of the proposed network, which achieves better visual quality against state-of-the-art methods. Full article
(This article belongs to the Special Issue Research Advances in Image Processing and Computer Vision)
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