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Mathematics
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New Advances in Image Processing and Computer Vision
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New Advances in 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: 31 October 2025 | Viewed by 2531

Special Issue Editors

Prof. Dr. Jorge Brieva

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Ciudad de México 03920, Mexico
Interests: image processing; non-contact vital sign monitoring; artificial intelligence; machine learning; biomedical imaging
Prof. Dr. Ernesto Moya-Albor

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Ciudad de México 03920, Mexico
Interests: image and signal processing; computer vision; machine learning; robotics navigation; biomedical engineering; cryptography; watermarking; motion estimation; motion magnification; fuzzy logic; contactless vital signs estimation

Special Issue Information

Dear Colleagues,

New mathematical models and methods used in the fields of image processing and computer vision are a key piece to describe and resolve many real-world problems where digital images are involved as the main source of information. These models and methods, in conjunction with edge-computer power and machine learning approaches, can solve complex image challenges.

This Special Issue focuses on the latest advances in the fields of image processing and computer vision. In addition, it provides a multidisciplinary platform for researchers and developers to share original, innovative, and state-of-the-art image processing and analysis algorithms and methods.

Papers on the following topics are welcome: image classification and segmentation, image enhancement, image restoration, image encryption, watermarking, texture analysis, document image processing, biomedical decision support systems, vision 3D, visual servoing, and video processing. Papers on machine learning models are also encouraged.

Prof. Dr. Jorge Brieva
Prof. Dr. Ernesto Moya-Albor
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 analysis and understanding
  • image enhancement
  • image segmentation
  • motion estimation and analysis
  • biomedical image processing applications
  • steganography and image watermarking
  • 3D vision
  • pattern recognition
  • object detection
  • machine learning applications

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

Published Papers (2 papers)

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19 pages, 8853 KiB  
Article
Automatic Neural Architecture Search Based on an Estimation of Distribution Algorithm for Binary Classification of Image Databases
by Erick Franco-Gaona, Maria Susana Avila-Garcia and Ivan Cruz-Aceves
Mathematics 2025, 13(4), 605; https://doi.org/10.3390/math13040605 - 12 Feb 2025
Viewed by 815
Abstract
Convolutional neural networks (CNNs) are widely used for image classification; however, setting the appropriate hyperparameters before training is subjective and time consuming, and the search space is not properly explored. This paper presents a novel method for the automatic neural architecture search based [...] Read more.
Convolutional neural networks (CNNs) are widely used for image classification; however, setting the appropriate hyperparameters before training is subjective and time consuming, and the search space is not properly explored. This paper presents a novel method for the automatic neural architecture search based on an estimation of distribution algorithm (EDA) for binary classification problems. The hyperparameters were coded in binary form due to the nature of the metaheuristics used in the automatic search stage of CNN architectures which was performed using the Boltzmann Univariate Marginal Distribution algorithm (BUMDA) chosen by statistical comparison between four metaheuristics to explore the search space, whose computational complexity is O(229). Moreover, the proposed method is compared with multiple state-of-the-art methods on five databases, testing its efficiency in terms of accuracy and F1-score. In the experimental results, the proposed method achieved an F1-score of 97.2%, 98.73%, 97.23%, 98.36%, and 98.7% in its best evaluation, better results than the literature. Finally, the computational time of the proposed method for the test set was ≈0.6 s, 1 s, 0.7 s, 0.5 s, and 0.1 s, respectively. Full article
(This article belongs to the Special Issue New Advances in Image Processing and Computer Vision)
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20 pages, 2900 KiB  
Article
HTTD: A Hierarchical Transformer for Accurate Table Detection in Document Images
by Mahmoud SalahEldin Kasem, Mohamed Mahmoud, Bilel Yagoub, Mostafa Farouk Senussi, Mahmoud Abdalla and Hyun-Soo Kang
Mathematics 2025, 13(2), 266; https://doi.org/10.3390/math13020266 - 15 Jan 2025
Viewed by 1171
Abstract
Table detection in document images is a challenging problem due to diverse layouts, irregular structures, and embedded graphical elements. In this study, we present HTTD (Hierarchical Transformer for Table Detection), a cutting-edge model that combines a Swin-L Transformer backbone with advanced Transformer-based mechanisms [...] Read more.
Table detection in document images is a challenging problem due to diverse layouts, irregular structures, and embedded graphical elements. In this study, we present HTTD (Hierarchical Transformer for Table Detection), a cutting-edge model that combines a Swin-L Transformer backbone with advanced Transformer-based mechanisms to achieve superior performance. HTTD addresses three key challenges: handling diverse document layouts, including historical and modern structures; improving computational efficiency and training convergence; and demonstrating adaptability to non-standard tasks like medical imaging and receipt key detection. Evaluated on benchmark datasets, HTTD achieves state-of-the-art results, with precision rates of 96.98% on ICDAR-2019 cTDaR, 96.43% on TNCR, and 93.14% on TabRecSet. These results validate its effectiveness and efficiency, paving the way for advanced document analysis and data digitization tasks. Full article
(This article belongs to the Special Issue New Advances in Image Processing and Computer Vision)
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