Application of Deep Learning and Computer Vision in Petrographic Images Analysis, 2nd Edition
A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Exploration Methods and Applications".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 48
Special Issue Editor
Interests: computer science; computer vision; image processing; image analysis; machine learning; artificial intelligence; software engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The first edition of Application of Deep Learning and Computer Vision in Petrographic Images Analysis was open for submissions from 2023 to 2024. The four published manuscripts covered a wide range of topics, from the segmentation of microscopic images to the classification of mineral matter. The application of artificial intelligence methods in petrography, particularly in the analysis of petrographic images, is a vibrant and developing field. Therefore, the idea of creating the next issue, which is particularly relevant to this research scope, has emerged and is now materializing with the launch of the second edition of the Special Issue. As Guest Editor, I warmly invite scholars to submit their latest studies for publication in the Special Issue.
Deep learning is becoming increasingly prevalent in the analysis of petrographic images. This relatively new approach reveals a wide range of possibilities for innovative ideas, groundbreaking research, and customized applications. Mineral identification, segmentation, and autonomous interpretation of thin-section petrographic images are a few examples of the many possibilities. Conversely, new network architectures are being developed that enable a significant increase in usage possibilities in petrography, particularly in petrographic image analysis. I believe this Special Issue will be an excellent place to share research results. Manuscripts relating to artificial intelligence, computer vision, deep learning, object detection, image segmentation, petrographic image analysis, maceral image analysis, and microscopic image analysis of mineral matter are very welcome.
Dr. Sebastian Iwaszenko
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. Minerals 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
- artificial intelligence
- computer vision
- deep learning
- object detection
- image segmentation
- petrographic images analysis
- maceral images analysis
- microscopic images of mineral matter analysis
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