Aims and scope

The journal publishes high-quality articles in areas of fundamental research in pattern recognition, machine learning, and their applications in medicine, science, and engineering.  The presented research should be analytically and experimentally well tested. In particular, it is essential to verify the quality of the proposed method correctly and compare it with state-of-the-art methods. It is also necessary to ensure that the methods are compared under precisely the same conditions. Thus, all articles should follow the replicable research principle, i.e., the ability for other researchers to repeat the experiment using the same methods and obtain similar results. This ensures the validity and reliability of the original findings. Therefore, providing access to the programming code (e.g., via GitHub) and the data used during the experiments is mandatory.

The journal publishes original papers and, from time to time, invited survey articles, in all areas related to pattern analysis, including, but not limited to, the following suggested topics:

    Machine Learning
    Statistical Pattern Recognition
    Neural Networks and Deep Learning
    Unsupervised and Semi-supervised Machine Learning
    Computer Vision and Image Processing
    Signal Processing
    Data Mining and Knowledge Discovery
    Natural Language Processing and Text Mining
    Security and Authentication Systems
    Medical and Biological Applications
    Science, Engineering, and Economic Applications

Special Issue on Mediterranean Conference on Pattern Recognition and Artificial Intelligence (MedPRAI 2025)
截稿日期: 2026-03-01

By invitation only.

This special issue features extended papers from the MedPRAI 2025 conference hosted in Istanbul, Turkey, in November 2025. Submission is by invitation only.

Please note that the authors of selected papers presented at MedPRAI 2025 will be invited to submit an extended version of their contributions by taking into consideration both the reviewers’ comments on their conference paper, and the feedback received during presentation at the conference. It is worth clarifying that the extended version is expected to contain a substantial scientific contribution, e.g., in the form of new algorithms, experiments or qualitative/quantitative comparisons, and that neither verbatim transfer of large parts of the conference paper nor reproduction of already published figures will be tolerated. The extended versions of MedPRAI 2025 papers will undergo the standard, rigorous journal review process and be accepted only if well-suited to the topic of this special issue and meeting the scientific level of the journal. Final decisions on all papers are made by the Editor-in-Chief.

Special Issue on Marine Vision
截稿日期: 2026-03-27

By invitation only.

Visually monitoring marine environments poses a vastly different task compared to monitoring terrestrial environments. It is physically challenging to acquire underwater data and the data typically have low signal-to-noise ratios due to the scattering nature of the water body. Nonetheless, it is critical that effective solutions for analyzing visual data from marine environments are developed as our oceans suffer at an unprecedented pace due to anthropogenic activities. Biologists, ecologists, oceanographers, fisheries managers, and many others would benefit greatly from robust, consistent, and accurate automation of marine image and video processing, which is currently, in most cases, done manually. The vision community has a compelling opportunity to significantly impact this important area of environmental science.

This special issue on Marine Vision contains extended journal editions of selected papers presented at ICCV25 and the Joint Workshop on Marine Vision (https://vap.aau.dk/marinevision/) held in conjunction with ICCV25. The articles--inter alia--are spanning the topics of:

Underwater

• image enhancement and reconstruction
• image formation models
• navigation and mapping
• 3D reconstruction of scenes
• calibration and parameter estimation
• (inverse) rendering and underwater scene representations
• physical models of reflectance and light transport, including multispectral or hyperspectral 

Marine

• fine-grained classifications of organisms
• foundation models
• datasets and evaluation
• learning from limited data
• object detection and segmentation