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Electronics
Special Issues
Applications in Computer Vision and Pattern Recognition
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Applications in Computer Vision and Pattern Recognition

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

Deadline for manuscript submissions: 15 June 2026 | Viewed by 601

Special Issue Editors

Dr. Chengliang Liu

E-Mail Website
Guest Editor
Laboratory for Artificial Intelligence in Design, The Hong Kong Polytechnic University, Hong Kong, China
Interests: machine learning; computer vision
Dr. Xiaoling Luo

E-Mail Website
Guest Editor
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518000, China
Interests: computer vision; artificial intelligence-assisted medical care

Special Issue Information

Dear Colleagues,

Computer vision and pattern recognition (CVPR) lie at the heart of enabling machines to perceive, understand, and reason about visual data. Despite remarkable progress, core challenges remain: learning generalizable representations from limited or noisy supervision; aligning visual perception with language and other modalities; modeling geometry, physics, and causality for robust reasoning; handling long-tailed categories and distribution shifts; ensuring reliability under occlusion, motion, and adverse conditions; and making models transparent, fair, and privacy-aware. Further exploration of the complex challenges in computer vision applications and pattern recognition is essential for moving beyond benchmark gains toward dependable perception and decision-making.

This Special Issue, “Applications in Computer Vision and Pattern Recognition” focuses on emerging methods that tighten the loop between representation learning, spatiotemporal reasoning, and real-world operation. We invite original research and comprehensive reviews on, but not limited to, the following:

  • Vision and language foundation models: Pretraining, adaptation, alignment, and safety evaluation.
  • Multimodal fusion: Feature fusion, joint decision-making, and trusted fusion.
  • 3D perception: SLAM, reconstruction, detection, segmentation.
  • Biomedical image processing and diagnosis.
  • Behavior pattern recognition: Gait recognition, sentiment analysis, and abnormal behavior detection.
  • Bioinformatics engineering, proteins, molecules, and genes.
  • Benchmarks and datasets essential for advancing CV for PR.
  • Open research problems and innovative solutions in CV for PR.

Dr. Chengliang Liu
Dr. Xiaoling Luo
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 250 words) can be sent to the Editorial Office for assessment.

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
  • pattern recognition
  • deep learning
  • machine learning
  • multimodal learning
  • biomedical engineering

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Published Papers (2 papers)

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Research

17 pages, 444 KB  
Article
Dynamic Quality Assessment-Based Multi-Feature Fusion
by Qilin Li, Yiyu Gong, Jungang You, Hongbin Hu, Chuan Peng, Dezhong Peng and Xuyang Wang
Electronics 2026, 15(3), 632; https://doi.org/10.3390/electronics15030632 - 2 Feb 2026
Viewed by 131
Abstract
To address the challenge in multi-view learning within practical application scenarios—such as smart grid multi-source monitoring and complex environment perception—where view quality often exhibits significant dynamic time-varying characteristics due to environmental interference or sensor failures, rendering traditional static fusion methods inadequate for maintaining [...] Read more.
To address the challenge in multi-view learning within practical application scenarios—such as smart grid multi-source monitoring and complex environment perception—where view quality often exhibits significant dynamic time-varying characteristics due to environmental interference or sensor failures, rendering traditional static fusion methods inadequate for maintaining decision-making reliability, a general adaptive robust fusion method, termed the Consensus-Aware Residual Gating (CARG) mechanism, is proposed. This approach constructs a sample-level dynamic quality assessment framework. It computes three interpretable metrics—self-confidence, group consensus, and complementary uniqueness—for each feature view in real time, thereby accurately quantifying instantaneous data quality fluctuations. A multiplicative gating structure is employed to generate dynamic weights based on these metrics, embedding a structural inductive bias of group consensus priority. Specifically, when quality degradation triggers view conflicts, the mechanism prioritizes majority-consistent reliable signals to suppress noise; when high-value complementary information emerges, it cautiously incentivizes discriminative features to rectify group bias. This design achieves adaptive perception of quality variations and robust decision-making without relying on additional weight-prediction networks. Extensive experiments are conducted on general multi-view benchmarks. The results demonstrate that CARG surpasses mainstream algorithms in accuracy, robustness, and interpretability. It effectively shields decisions from anomalous feature interference and validates its efficacy as a universal fusion framework for dynamic environments. Full article
(This article belongs to the Special Issue Applications in Computer Vision and Pattern Recognition)
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16 pages, 589 KB  
Article
Enhanced Tensor Incomplete Multi-View Clustering with Dual Adaptive Weight
by Jiongcheng Zhu, Wenzhe Liu, Zhenyu Xu and Changjun Zhou
Electronics 2026, 15(1), 9; https://doi.org/10.3390/electronics15010009 - 19 Dec 2025
Viewed by 266
Abstract
In practical application, the gathered multi-view data typically misses samples, known as incomplete multi-view data. Most existing incomplete multi-view clustering methods obtain consensus information in multi-view data by completing incomplete data using zero, mean values, etc. These approaches often ignore the higher-order relationship [...] Read more.
In practical application, the gathered multi-view data typically misses samples, known as incomplete multi-view data. Most existing incomplete multi-view clustering methods obtain consensus information in multi-view data by completing incomplete data using zero, mean values, etc. These approaches often ignore the higher-order relationship and structural information between different views. To alleviate the above problems, we propose enhanced tensor incomplete multi-view clustering with dual adaptive weight (ETIMC), which can acquire the higher-order relationship, and structural information between multiple perspectives, adaptively recover the missing samples and distinguish the contribution degree of different views. Specifically, the embedded representations obtained from incomplete multi-view data are stacked into a third-order tensor to capture the higher-order relationship. Then, a consensus matrix can be drawn from these potential representations via a self-weighting mechanism. Additionally, we adaptively reconstruct the missing samples while capturing structural information by the hypergraph Laplacian item. Moreover, we integrate the embedded representation of each view, tensor constraints, hypergraph Laplacian regularization, and dual adaptive weighted mechanisms into a unified framework. Experimental results on natural and synthetic incomplete datasets show the superiority of ETIMC. Full article
(This article belongs to the Special Issue Applications in Computer Vision and Pattern Recognition)
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