Advances in Machine Learning for Computer Vision Applications

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Computer Vision and Pattern Recognition".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 172

Special Issue Editors


E-Mail Website
Guest Editor
Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
Interests: machine learning; artificial intelligence; computational learning theory; computer vision; natural language processing; reality-based algebras; Hopf algebra; representation theory
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada
Interests: artificial intelligence; machine learning; computer vision; electric power systems; time series forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on recent developments in machine learning for computer vision applications. Researchers are encouraged to submit their latest research papers on topics including deep learning for image and video analysis, object detection and recognition, and scene understanding. Studies on real-world applications of machine learning and computer vision in various fields, including medical diagnosis, autonomous vehicles, robotics, surveillance, and augmented reality, are also welcome. 

Dr. Gurmail Singh
Dr. Stéfano Frizzo Stefenon
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. Journal of Imaging 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 1800 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
  • machine learning
  • deep learning
  • image processing
  • object detection and recognition
  • scene understanding
  • video analysis
  • autonomous systems
  • robotics
  • augmented reality
  • surveillance
  • pattern recognition
  • neural networks
  • data-driven vision

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

22 pages, 3354 KiB  
Article
PS-YOLO-seg: A Lightweight Instance Segmentation Method for Lithium Mineral Microscopic Images Based on Improved YOLOv12-seg
by Zeyang Qiu, Xueyu Huang, Zhicheng Deng, Xiangyu Xu and Zhenzhong Qiu
J. Imaging 2025, 11(7), 230; https://doi.org/10.3390/jimaging11070230 - 10 Jul 2025
Abstract
Microscopic image automatic recognition is a core technology for mineral composition analysis and plays a crucial role in advancing the intelligent development of smart mining systems. To overcome the limitations of traditional lithium ore analysis and meet the challenges of deployment on edge [...] Read more.
Microscopic image automatic recognition is a core technology for mineral composition analysis and plays a crucial role in advancing the intelligent development of smart mining systems. To overcome the limitations of traditional lithium ore analysis and meet the challenges of deployment on edge devices, we propose PS-YOLO-seg, a lightweight segmentation model specifically designed for lithium mineral analysis under visible light microscopy. The network is compressed by adjusting the width factor to reduce global channel redundancy. A PSConv-based downsampling strategy enhances the network’s ability to capture dim mineral textures under microscopic conditions. In addition, the improved C3k2-PS module strengthens feature extraction, while the streamlined Segment-Efficient head minimizes redundant computation, further reducing the overall model complexity. PS-YOLO-seg achieves a slightly improved segmentation performance compared to the baseline YOLOv12n model on a self-constructed lithium ore microscopic dataset, while reducing FLOPs by 20%, parameter count by 33%, and model size by 32%. Additionally, it achieves a faster inference speed, highlighting its potential for practical deployment. This work demonstrates how architectural optimization and targeted enhancements can significantly improve instance segmentation performance while maintaining speed and compactness, offering strong potential for real-time deployment in industrial settings and edge computing scenarios. Full article
(This article belongs to the Special Issue Advances in Machine Learning for Computer Vision Applications)
Show Figures

Figure 1

Back to TopTop