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Applied Sciences
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Intelligence Image Processing and Patterns Recognition
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Intelligence Image Processing and Patterns Recognition

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 578

Special Issue Editor

Prof. Dr. Xianye Ben

E-Mail Website
Guest Editor
School of Information Science and Engineering, Shandong University, Qingdao 266237, China
Interests: pattern recognition; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The past decades have witnessed the great success of image processing and pattern recognition in many fields such as image denoising, image synthetization and translation, biometric recognition, etc. Automated image analysis and information extraction from mass real-world data have significantly increased productivity in practical engineering applications. Especially, deep learning and other advanced methods are accelerating the revolution. Computational intelligence will play a key role in the revolution due to its potential power in information processing, decision making and knowledge management.

This Special Issue will gather recent advances in both theoretical and practical studies of computational intelligence, emphasizing image processing and pattern recognition. Potential topics include, but are not limited to, the use of computational intelligence techniques such as neural networks, fuzzy logic, metaheuristics and expert systems in the following topics:

(1) Image processing, including morphology, filtering and enhancement, etc.;

(2) Supervised/semi-supervised/unsupervised learning;

(3) Reinforcement learning;

(4) Deep learning theory and applications;

(5) Pattern recognition;

(6) Computer vision;

(7) Natural language processing;

(8) Time-series analysis;

(9) Data mining.

Prof. Dr. Xianye Ben
Guest Editor

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. 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

  • image processing
  • pattern recognition
  • machine learning
  • deep learning
  • computer vision natural language processing
  • data mining

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Published Papers (1 paper)

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Research

19 pages, 1486 KiB  
Article
A Dual-Enhanced Hierarchical Alignment Framework for Multimodal Named Entity Recognition
by Jian Wang, Yanan Zhou, Qi He and Wenbo Zhang
Appl. Sci. 2025, 15(11), 6034; https://doi.org/10.3390/app15116034 - 27 May 2025
Viewed by 418
Abstract
Multimodal amed entity recognition (MNER) is a natural language-processing technique that integrates text and visual modalities to detect and segment entity boundaries and their types from unstructured multimodal data. Although existing methods alleviate semantic deficiencies by optimizing image and text feature extraction and [...] Read more.
Multimodal amed entity recognition (MNER) is a natural language-processing technique that integrates text and visual modalities to detect and segment entity boundaries and their types from unstructured multimodal data. Although existing methods alleviate semantic deficiencies by optimizing image and text feature extraction and fusion, a fundamental challenge remains due to the lack of fine-grained alignment caused by cross-modal semantic deviations and image noise interference. To address these issues, this paper proposes a dual-enhanced hierarchical alignment (DEHA) framework that achieves dual semantic and spatial enhancement via global–local cooperative alignment optimization. The proposed framework incorporates a dual enhancement strategy comprising Semantic-Augmented Global Contrast (SAGC) and Multi-scale Spatial Local Contrast (MS-SLC), which reinforce the alignment of image and text modalities at the global sample level and local feature level, respectively, thereby reducing image noise. Additionally, a cross-modal feature fusion and vision-constrained CRF prediction layer is designed to achieve adaptive aggregation of global and local features. Experimental results on the Twitter-2015 and Twitter-2017 datasets yield F1 scores of 77.42% and 88.79%, outperforming baseline models. These results demonstrate that the global–local complementary mechanism effectively balances alignment precision and noise robustness, thereby enhancing entity recognition accuracy in social media and advancing multimodal semantic understanding. Full article
(This article belongs to the Special Issue Intelligence Image Processing and Patterns Recognition)
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