Application of Deep Learning and Computer Vision in Petrographic Images Analysis
A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Processing and Extractive Metallurgy".
Deadline for manuscript submissions: 30 September 2024 | Viewed by 1229
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,
Artificial intelligence and computer vision are becoming indispensable components of our everyday life. This is also particularly true in the case of scientific research, where the application of their achievements not only supports work automation but also creates opportunities for discoveries not possible before.
One such scientific field where computer vision and in particular deep learning is being more and more present is in the analysis of petrographic images. Mineral identification, segmentation, and autonomous interpretation of the thin section petrographic images are only a few examples of many potentials (and nowadays ongoing) applications. Therefore, in this Special Issue, we aim to include original and recent work or reviews in the form of methodologies, technologies, or applications of computer vision and that demonstrate a particular focus on deep learning in petrography. The wide and important area of image analysis via the use of artificial intelligence methods is an exciting field of research. We believe that this Special Issue will be an excellent place to share the research results. We welcome manuscripts relating, but not limited to, the following areas: artificial intelligence, computer vision, deep learning, object detection, image segmentation, petrographic images analysis, maceral images analysis, and microscopic images of mineral matter analysis.
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
