Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.0
2.3
Journals
Mathematics
Special Issues
Mathematical Methods for Image Processing and Computer Vision
share announcement

Mathematical Methods for Image Processing and Computer Vision

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 12 October 2025 | Viewed by 2198

Special Issue Editors

Dr. Rogelio Hasimoto-Beltran

E-Mail Website
Guest Editor
Center for Research in Mathematics (CIMAT), Apartado Postal 402, Guanajuato CP 36000, Mexico
Interests: image processing; chaotic security; computer networking
Dr. Oscar Susano Dalmau Cedeño

E-Mail Website
Guest Editor
Centro de Investigación en Matemáticas, A.C., Jalisco S/N, Col. Valenciana, Guanajuato CP 36023, Mexico
Interests: numerical optimization; machine learning and image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in the field of digital Image Processing and Computer Vision (IPCV) have contributed to the development of virtually every branch of science for which imaging technologies are applicable. This collaboration has led to the realization of unimaginable problem-solving capabilities that closely resemble (up to a certain level) common human vision tasks and behaviors. Typical applications include object recognition and tracking, autonomous navigation, accident prevention, face detection and recognition, security and surveillance, medical diagnoses, the remote sensing of the environment, and many more. IPVC has empowered digital machines to manipulate, transform, enhance, extract, and analyze image semantics by using both classical mathematical models and artificial intelligence schemes, such as Deep Neural Networks. These IPVC systems represent a valuable asset for the automation of image and vision-related tasks in various domains, enhancing their accuracy, efficiency, and decision-making capabilities in real-time.

This Special Issue welcomes the submission of original research and review articles on the development and advancement of mathematical and artificial intelligence approaches, techniques, and models in the field of image processing and computer vision.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Mathematical and statistical models;
  • Low-level vision;
  • Filtering, enhancement, and restoration (denoising, deblurring, error concealment, etc.);
  • Stereo vision and Super-resolution;
  • Segmentation, grouping, and shape analysis;
  • Biomedical diagnostic and imaging;
  • Remote sensing;
  • Image and video synthesis and generation;
  • Multimodal Learning;
  • Security and surveillance;
  • Industrial automation;
  • Multi-scale analysis and decomposition;
  • Mathematical morphology and topology;
  • Machine learning and deep learning.

Dr. Rogelio Hasimoto-Beltran
Dr. Oscar Susano Dalmau Cedeño
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • image processing
  • computer vision
  • mathematical morphology and topology
  • machine learning and deep learning

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 2692 KiB  
Article
Unit Size Determination for Exploratory Brain Imaging Analysis: A Quest for a Resolution-Invariant Metric
by Jihnhee Yu, HyunAh Lee and Zohi Sternberg
Mathematics 2025, 13(7), 1195; https://doi.org/10.3390/math13071195 - 4 Apr 2025
Viewed by 232
Abstract
Defining an adequate unit size is often crucial in brain imaging analysis, where datasets are complex, high-dimensional, and computationally demanding. Unit size refers to the spatial resolution at which brain data is aggregated for analysis. Optimizing unit size in data aggregation requires balancing [...] Read more.
Defining an adequate unit size is often crucial in brain imaging analysis, where datasets are complex, high-dimensional, and computationally demanding. Unit size refers to the spatial resolution at which brain data is aggregated for analysis. Optimizing unit size in data aggregation requires balancing computational efficiency in handling large-scale data sets with the preservation of brain activity patterns, minimizing signal dilution. We propose using the Calinski–Harabasz index, demonstrating its invariance to sample size changes due to varying image resolutions when no distributional differences are present, while the index effectively identifies an appropriate unit size for detecting suspected regions in image comparisons. The resolution-independent metric can be used for unit size evaluation, ensuring adaptability across different imaging protocols and modalities. This study enhances the scalability and efficiency of brain imaging research by providing a robust framework for unit size optimization, ultimately strengthening analytical tools for investigating brain function and structure. Full article
(This article belongs to the Special Issue Mathematical Methods for Image Processing and Computer Vision)
►▼ Show Figures

Figure 1

16 pages, 1852 KiB  
Article
Universal Network for Image Registration and Generation Using Denoising Diffusion Probability Model
by Huizhong Ji, Peng Xue and Enqing Dong
Mathematics 2024, 12(16), 2462; https://doi.org/10.3390/math12162462 - 9 Aug 2024
Cited by 1 | Viewed by 1472
Abstract
Classical diffusion model-based image registration approaches require separate diffusion and deformation networks to learn the reverse Gaussian transitions and predict deformations between paired images, respectively. However, such cascaded architectures introduce noisy inputs in the registration, leading to excessive computational complexity and issues with [...] Read more.
Classical diffusion model-based image registration approaches require separate diffusion and deformation networks to learn the reverse Gaussian transitions and predict deformations between paired images, respectively. However, such cascaded architectures introduce noisy inputs in the registration, leading to excessive computational complexity and issues with low registration accuracy. To overcome these limitations, a diffusion model-based universal network for image registration and generation (UNIRG) is proposed. Specifically, the training process of the diffusion model is generalized as a process of matching the posterior mean of the forward process to the modified mean. Subsequently, the equivalence between the training process for image generation and that for image registration is verified by incorporating the deformation information of the paired images to obtain the modified mean. In this manner, UNIRG integrates image registration and generation within a unified network, achieving shared training parameters. Experimental results on 2D facial and 3D cardiac medical images demonstrate that the proposed approach integrates the capabilities of image registration and guided image generation. Meanwhile, UNIRG achieves registration performance with NMSE of 0.0049, SSIM of 0.859, and PSNR of 27.28 on the 2D facial dataset, along with Dice of 0.795 and PSNR of 12.05 on the 3D cardiac dataset. Full article
(This article belongs to the Special Issue Mathematical Methods for Image Processing and Computer Vision)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop