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Applied Sciences
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Advanced Pattern Recognition & Computer Vision, 2nd Edition
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Advanced Pattern Recognition & Computer Vision, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 853

Special Issue Editors

Dr. Hancheng Zhu

E-Mail Website
Guest Editor
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
Interests: computer vision; deep learning; image aesthetic quality assessment; affective computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rui Yao

E-Mail Website
Guest Editor
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
Interests: computer vision; deep learning; object segmentation; visual tracking; image classification; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Pattern recognition and computer vision are long-term research hotspots, which have a wide range of application scenarios in real life. Recently, deep learning has become the core technology for pattern recognition and computer vision tasks. Although these deep learning models have achieved remarkable success in fields such as multimedia data recognition and analysis. However, the existing technology mainly obtains promising performance from a data-driven perspective. Therefore, advanced pattern recognition and computer vision methods are urgently needed in relevant research fields. Future studies should seek the application of general-purpose deep models with interpretable knowledge learning to pattern recognition and computer vision tasks, such as object detection, image analysis and understanding. In this Special Issue, we are particularly interested in advanced pattern recognition and computer vision approaches.

Topics of interest include but are not limited to:

  • Image classification and segmentation;
  • Object detection and tracking;
  • Image quality assessment and enhancement;
  • Image denoising and reconstruction;
  • Psychophysical analysis of visual perception;
  • Image generation and super-resolution;
  • Visual data reduction and compression;
  • Deep learning for computer vision tasks (medical image processing, remote sensing, hyperspectral imaging, thermal imaging);
  • multimedia affective computing;
  • RGB-D and 3D processing;
  • Interpretable deep learning models.

Dr. Hancheng Zhu
Prof. Dr. Rui Yao
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. Applied Sciences 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

  • pattern recognition
  • computer vision
  • deep learning
  • image processing
  • object detection

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Published Papers (2 papers)

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20 pages, 15155 KB  
Article
Robust Image Watermarking via Clustered Visual State-Space Modeling
by Bo Liu and Jianhua Ren
Appl. Sci. 2026, 16(9), 4166; https://doi.org/10.3390/app16094166 - 24 Apr 2026
Viewed by 149
Abstract
Most existing DNN-based image watermarking methods adopt an “encoder–noise–decoder” paradigm, where the watermark is typically replicated and expanded in a straightforward manner and then directly fused with image features, which limits robustness under complex distortions. Although Transformers improve fusion via attention mechanisms, their [...] Read more.
Most existing DNN-based image watermarking methods adopt an “encoder–noise–decoder” paradigm, where the watermark is typically replicated and expanded in a straightforward manner and then directly fused with image features, which limits robustness under complex distortions. Although Transformers improve fusion via attention mechanisms, their quadratic computational complexity makes high-resolution processing prohibitively expensive. To address these issues, we propose CCViM, a robust watermarking framework built on Vision Mamba, which leverages the linear-complexity property of state-space models (SSMs) to enable efficient global interactions. We design a Watermark Representation Learning Module (WRLM) that performs hierarchical feature extraction and structured expansion of the watermark through cascaded VSS blocks, yielding semantically rich and perturbation-resistant watermark representations. In addition, we introduce an Interwoven Fusion Enhancement Module (IFEM), which employs a CCS6 structure to treat the watermark as a dynamic guidance signal. By combining contextual clustering with the Mamba mechanism, IFEM deeply interweaves the watermark into host features at both local and global levels. Experiments on COCO, DIV2K, and ImageNet demonstrate that CCViM consistently improves imperceptibility, robustness, and efficiency to varying degrees, and remains stable and high quality under attacks such as JPEG compression, cropping, and Gaussian blur. Full article
(This article belongs to the Special Issue Advanced Pattern Recognition & Computer Vision, 2nd Edition)
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20 pages, 1198 KB  
Article
ADCT: Improving Robustness and Calibration of Pattern Recognition Models Against Visual Illusions
by Hui Dong, Lin Yu and Yi Yang
Appl. Sci. 2026, 16(5), 2164; https://doi.org/10.3390/app16052164 - 24 Feb 2026
Viewed by 302
Abstract
Perception-level interference patterns, such as abutting gratings that induce illusory contours and pseudoisochromatic dot camouflage, can trigger failures that are not well captured by conventional corruption benchmarks. We construct an illusion-driven evaluation suite based on MNIST variants, including AG-MNIST and Ishihara-MNIST, under a [...] Read more.
Perception-level interference patterns, such as abutting gratings that induce illusory contours and pseudoisochromatic dot camouflage, can trigger failures that are not well captured by conventional corruption benchmarks. We construct an illusion-driven evaluation suite based on MNIST variants, including AG-MNIST and Ishihara-MNIST, under a unified 224×224×3 pipeline with fixed train/validation/test splits. Building on a multi-domain empirical risk minimization (ERM) baseline, we present AugMix–DeepAugment Consistency Training (ADCT), a training-time recipe that combines mixed augmentations, DeepAugment-style image distortions, and Jensen–Shannon consistency regularization. Across multiple ImageNet-pretrained backbones and multiple random seeds, ADCT improves robustness on the five-set illusion OOD suite (OOD5) on average while simultaneously improving probabilistic calibration, as measured by ECE together with proper scoring rules (NLL and Brier score). For ResNet-50, ADCT yields a substantial gain on OOD5 relative to the Ishihara-only baseline (S0), increasing accuracy from 29.0% to 59.7% and reducing NLL from 7.44 to 1.11. To assess external validity, we additionally report results on CIFAR-10 and CIFAR-10-C and compare against representative augmentation-based baselines (including PixMix), contextualizing the robustness–calibration trade-off on a widely used natural-image robustness benchmark. These results suggest that consistency-based augmentation recipes can improve both robustness and confidence reliability under structured, illusion-like shifts without changing inference-time architectures. Full article
(This article belongs to the Special Issue Advanced Pattern Recognition & Computer Vision, 2nd Edition)
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