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Electronics
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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 January 2027 | Viewed by 2163

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-anonymized 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 (4 papers)

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Research

27 pages, 11736 KB  
Article
KPP-BA: A Key-Dependent Pixel Permutation and Parity-Based Authentication Framework for Medical Image Tamper Detection
by Chia-Chen Lin, En-Ting Chu and Er-Tai Zhuo
Electronics 2026, 15(12), 2732; https://doi.org/10.3390/electronics15122732 - 21 Jun 2026
Viewed by 186
Abstract
With the prevalence of telemedicine and digital diagnosis, the security and integrity of medical images transmitted over open networks have become critical issues. To effectively defend against malicious tampering and ensure the reliability of diagnostic information, this study proposes a block-based image authentication [...] Read more.
With the prevalence of telemedicine and digital diagnosis, the security and integrity of medical images transmitted over open networks have become critical issues. To effectively defend against malicious tampering and ensure the reliability of diagnostic information, this study proposes a block-based image authentication and tamper detection framework (KPP-BA). This framework integrates key-dependent pixel permutation, hash-based message authentication code (HMAC)-SHA256 hash verification, and a parity-based 3-LSB minimal distortion embedding strategy. The core innovation lies in utilizing pseudo-random pixel permutation to disrupt spatial correlation within blocks, thereby effectively resisting collage and statistical analysis attacks. Furthermore, by combining the avalanche effect of HMAC-SHA256 with hybrid bit-plane feature extraction, the proposed method ensures extremely high sensitivity to subtle tampering. Experimental results on a dataset comprising 300 medical images demonstrate that the proposed method maintains superior visual quality while ensuring security, achieving an average Peak Signal-to-Noise Ratio (PSNR) of 54.15 of 0.5 bit per pixel (bpp). Moreover, against various tampering attacks—including masking, copy–paste, circle masking, and collage—the method exhibits exceptional detection capabilities with an average detection accuracy of 99.99%. Compared with seven state-of-the-art methods, the proposed framework demonstrates significant advantages in both image fidelity and tamper localization precision, validating its feasibility and robustness for secure medical image transmission applications. Full article
(This article belongs to the Special Issue Applications in Computer Vision and Pattern Recognition)
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18 pages, 844 KB  
Article
EGD: Error-Entropy-Guided Distillation for Noisy Multi-View Classification
by Xiaoyu Yang, Yanan Li, Shilin Xu and Yuan Sun
Electronics 2026, 15(8), 1596; https://doi.org/10.3390/electronics15081596 - 10 Apr 2026
Cited by 1 | Viewed by 402
Abstract
In recent years, multi-view learning has received extensive research interest. Most existing multi-view learning methods often rely on well-annotated data to improve decision accuracy. However, noisy labels are ubiquitous in multi-view data due to imperfect annotations. Although some methods have achieved promising performance [...] Read more.
In recent years, multi-view learning has received extensive research interest. Most existing multi-view learning methods often rely on well-annotated data to improve decision accuracy. However, noisy labels are ubiquitous in multi-view data due to imperfect annotations. Although some methods have achieved promising performance using robust-loss designs and implicit regularization, they fail to explicitly model the reliability of the supervision signal and fail to dynamically correct noisy labels during training. Clearly, this largely constrains their performance ceiling. To deal with this problem, we propose an Error-Entropy-Guided Distillation network (EGD) for noisy multi-view classification. In this framework, we first design an Error-Entropy (EE) metric to explicitly evaluate the reliability of sample-wise supervision, which serves as the basis for identifying and filtering noisy labels. On this basis, we adopt the distillation paradigm based on Error-Entropy (EE). The teacher model provides the student with soft label distributions that are less affected by noisy labels in the early training stage. To further mitigate noise memorization and accumulated confirmation bias, we propose a periodic memory-clearing strategy and supervision signal update strategy to prevent the teacher from error memorization and accumulating confirmation bias. Meanwhile, the student model learns from the soft supervision of the teacher to capture structured inter-class relationships. Additionally, a consistency module is employed to enhance the consistency of the student across multiple views. Extensive experiments on five benchmark datasets demonstrate that EGD consistently outperforms state-of-the-art multi-view learning methods under various noise levels. Full article
(This article belongs to the Special Issue Applications in Computer Vision and Pattern Recognition)
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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 514
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
Cited by 1 | Viewed by 621
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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