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Symmetry
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Symmetry and Asymmetry in Artificial Intelligence and Machine Learning-Based Image...
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Symmetry and Asymmetry in Artificial Intelligence and Machine Learning-Based Image Processing

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1621

Special Issue Editors

Prof. Dr. Panagiotis Adamidis

E-Mail Website
Guest Editor
Department of Information and Electronic Engineering, International Hellenic University, 57400 Thessaloniki, Greece
Interests: evolutionary computation; machine learning; computational humor; team formation; scheduling and timetabling problems
Dr. Keiko Ono

E-Mail Website
Guest Editor
Faculty of Science and Engineering, Doshisha University, Kyoto 610-0321, Japan
Interests: genetic programming; group intelligence; optimization

Special Issue Information

Dear Colleagues,

In AI and machine learning-based image processing, symmetry and asymmetry are fundamental properties that significantly impact pattern recognition, feature extraction, and algorithmic performance. This Special Issue, "Symmetry and Asymmetry in Artificial Intelligence and Machine Learning-Based Image Processing", explores how these properties can enhance modern computational imaging systems.

We welcome innovative research contributions that advance our understanding of how AI and ML algorithms can effectively leverage symmetrical and asymmetrical patterns encompassing both theoretical frameworks and practical applications.

Of particular interest are cutting-edge approaches to symmetry detection, asymmetric pattern recognition, and their real-world applications across diverse domains, including medical imaging, autonomous driving, computer vision, and industrial automation. We seek submissions that demonstrate how symmetry and asymmetry principles can improve the robustness, efficiency, and performance of AI-driven image processing solutions.

We invite original research articles, comprehensive reviews, and case studies that explore novel methodologies, theoretical advances, or practical implementations. Submissions may emphasize how symmetry/asymmetry considerations can advance the state-of-the-art in AI and ML-based image processing.

Prof. Dr. Panagiotis Adamidis
Dr. Keiko Ono
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. Symmetry is an international peer-reviewed open access monthly 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

  • symmetry-aware deep learning
  • asymmetric feature detection
  • feature extraction
  • object detection
  • symmetry-guided attention
  • symmetry-based segmentation
  • deep feature learning
  • geometric invariance
  • pattern recognition
  • image segmentation
  • computer vision
  • image transformations

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

20 pages, 37686 KiB  
Article
Multi-Source Training-Free Controllable Style Transfer via Diffusion Models
by Cuihong Yu, Cheng Han and Chao Zhang
Symmetry 2025, 17(2), 290; https://doi.org/10.3390/sym17020290 - 13 Feb 2025
Cited by 1 | Viewed by 1212
Abstract
Diffusion models, as representative models in the field of artificial intelligence, have made significant progress in text-to-image synthesis. However, studies of style transfer using diffusion models typically require a large amount of text to describe semantic content or specific painting attributes, and the [...] Read more.
Diffusion models, as representative models in the field of artificial intelligence, have made significant progress in text-to-image synthesis. However, studies of style transfer using diffusion models typically require a large amount of text to describe semantic content or specific painting attributes, and the style and layout of semantic content in synthesized images are frequently uncertain. To accomplish high-quality fixed content style transfer, this paper adopts text-free guidance and proposes a multi-source, training-free and controllable style transfer method by using single image or video as content input and single or multiple style images as style guidance. To be specific, the proposed method firstly fuses the inversion noise of a content image with that of a single or multiple style images as the initial noise of stylized image sampling process. Then, the proposed method extracts the self-attention mechanism’s query, key, and value vectors from the DDIM inversion process of content and style images and injects them into the stylized image sampling process to improve the color, texture and semantics of stylized images. By setting the hyperparameters involved in the proposed method, the style transfer effect of symmetric style proportion and asymmetric style distribution can be achieved. By comparing with state-of-the-art baselines, the proposed method demonstrates high fidelity and excellent stylized performance, and can be applied to numerous image or video style transfer tasks. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Artificial Intelligence and Machine Learning-Based Image Processing)
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