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Computers
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Advanced Image Processing and Computer Vision (2nd Edition)
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Advanced Image Processing and Computer Vision (2nd Edition)

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 31 July 2025 | Viewed by 800

Special Issue Editors

Dr. Selene Tomassini

E-Mail Website
Guest Editor
Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123 Povo, TN, Italy
Interests: advanced image processing; artificial intelligence; computer-aided detection and diagnosis; computer vision; decision-support systems; medical imaging
Special Issues, Collections and Topics in MDPI journals
Dr. M. Ali Akber Dewan

E-Mail Website
Guest Editor
School of Computing and Information Systems, Faculty of Science and Technology, Athabasca University, Athabasca, AB T9S 3A3, Canada
Interests: image processing; computer vision; machine learning; pattern recognition; adaptive biometrics; artificial intelligence; medical image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced image processing (AIP) and computer vision (CV) are broad research topics, which continue to have impacts and generate innovation in a wide range of real-world applications.

Nowadays, artificial intelligence (AI) permeates our daily activities. Among AI-based algorithms, convolutional neural networks and vision transformers are currently the core technologies for AIP and CV, often combined with recurrent modules or large language models in case of multimodal inputs. Although these AI-based algorithms have achieved remarkable success, the existing technology has promising performance from a data-driven perspective. Thus, novel AI-based algorithms for AIP and CV are urgently needed, especially in those research fields in which data scarcity, data quality and/or data interpretation and understanding are concrete issues. Furthermore, the need for a human-centric AI approach should be highlighted, ensuring that the technology is ethical, explainable and fair.

As a prosecution of the successful Special Issue “Advanced Image Processing and Computer Vision”, this 2nd Edition aims to collect original scientific articles, literature reviews and in-depth technical reports on the design, development and/or deployment of AI-based algorithms for AIP and CV in real-world application scenarios, with a particular, though not sole, focus on medical scenarios.

Dr. Selene Tomassini
Dr. M. Ali Akber Dewan
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. Computers 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 1800 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

  • advanced image processing
  • artificial intelligence, machine learning and deep learning
  • computational models
  • computer-aided detection, segmentation and diagnosis
  • computer vision
  • decision-support systems
  • ethics and explainability
  • federated learning paradigms
  • generative models
  • image analysis, interpretation and understanding
  • image-guided decision, planning and treatment
  • image pre- and post-processing
  • medical image-guided interventions
  • medical image processing and analysis
  • on-the-edge artificial intelligence
  • open-access imaging datasets

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Related Special Issue

Published Papers (3 papers)

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35 pages, 64566 KiB  
Article
A Novel MaxViT Model for Accelerated and Precise Soybean Leaf and Seed Disease Identification
by Al Shahriar Uddin Khondakar Pranta, Hasib Fardin, Jesika Debnath, Amira Hossain, Anamul Haque Sakib, Md. Redwan Ahmed, Rezaul Haque, Ahmed Wasif Reza and M. Ali Akber Dewan
Computers 2025, 14(5), 197; https://doi.org/10.3390/computers14050197 (registering DOI) - 18 May 2025
Abstract
Timely diagnosis of soybean diseases is essential to protect yields and limit global economic loss, yet current deep learning approaches suffer from small, imbalanced datasets, single‑organ focus, and limited interpretability. We propose MaxViT‑XSLD (MaxViT XAI-Seed–Leaf-Diagnostic), a Vision Transformer that integrates multiaxis attention with [...] Read more.
Timely diagnosis of soybean diseases is essential to protect yields and limit global economic loss, yet current deep learning approaches suffer from small, imbalanced datasets, single‑organ focus, and limited interpretability. We propose MaxViT‑XSLD (MaxViT XAI-Seed–Leaf-Diagnostic), a Vision Transformer that integrates multiaxis attention with MBConv layers to jointly classify soybean leaf and seed diseases while remaining lightweight and explainable. Two benchmark datasets were upscaled through elastic deformation, Gaussian noise, brightness shifts, rotation, and flipping, enlarging ASDID from 10,722 to 16,000 images (eight classes) and the SD set from 5513 to 10,000 images (five classes). Under identical augmentation and hyperparameters, MaxViT‑XSLD delivered 99.82% accuracy on ASDID and 99.46% on SD, surpassing competitive ViT, CNN, and lightweight SOTA variants. High PR‑AUC and MCC values, confirmed via 10‑fold stratified cross‑validation and Wilcoxon tests, demonstrate robust generalization across data splits. Explainable AI (XAI) techniques further enhanced interpretability by highlighting biologically relevant features influencing predictions. Its modular design also enables future model compression for edge deployment in resource‑constrained settings. Finally, we deploy the model in SoyScan, a real‑time web tool that streams predictions and visual explanations to growers and agronomists. These findings establishes a scalable, interpretable system for precision crop health monitoring and lay the groundwork for edge‑oriented, multimodal agricultural diagnostics. Full article
(This article belongs to the Special Issue Advanced Image Processing and Computer Vision (2nd Edition))
16 pages, 3176 KiB  
Article
On Generating Synthetic Datasets for Photometric Stereo Applications
by Elisa Crabu and Giuseppe Rodriguez
Computers 2025, 14(5), 166; https://doi.org/10.3390/computers14050166 - 29 Apr 2025
Viewed by 162
Abstract
The mathematical model for photometric stereo makes several restricting assumptions, which are often not fulfilled in real-life applications. Specifically, an object surface does not always satisfies Lambert’s cosine law, leading to reflection issues. Moreover, the camera and the light source, in some situations, [...] Read more.
The mathematical model for photometric stereo makes several restricting assumptions, which are often not fulfilled in real-life applications. Specifically, an object surface does not always satisfies Lambert’s cosine law, leading to reflection issues. Moreover, the camera and the light source, in some situations, have to be placed at a close distance from the target, rather than at infinite distance from it. When studying algorithms for these complex situations, it is extremely useful to have at disposal synthetic datasets with known exact solutions, to assert the accuracy of a solution method. The aim of this paper is to present a Matlab package which constructs such datasets on the basis of a chosen exact solution, providing a tool for simulating various real camera/light configurations. This package, starting from the mathematical expression of a surface, or from a discrete sampling, allows the user to build a set of images matching a particular light configuration. Setting various parameters makes it possible to simulate different scenarios, which can be used to investigate the performance of reconstruction algorithms in several situations and test their response to lack of ideality in data. The ability to construct large datasets is particularly useful to train machine learning based algorithms. Full article
(This article belongs to the Special Issue Advanced Image Processing and Computer Vision (2nd Edition))
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19 pages, 13596 KiB  
Article
SMS3D: 3D Synthetic Mushroom Scenes Dataset for 3D Object Detection and Pose Estimation
by Abdollah Zakeri, Bikram Koirala, Jiming Kang, Venkatesh Balan, Weihang Zhu, Driss Benhaddou and Fatima A. Merchant
Computers 2025, 14(4), 128; https://doi.org/10.3390/computers14040128 - 1 Apr 2025
Viewed by 340
Abstract
The mushroom farming industry struggles to automate harvesting due to limited large-scale annotated datasets and the complex growth patterns of mushrooms, which complicate detection, segmentation, and pose estimation. To address this, we introduce a synthetic dataset with 40,000 unique scenes of white Agaricus [...] Read more.
The mushroom farming industry struggles to automate harvesting due to limited large-scale annotated datasets and the complex growth patterns of mushrooms, which complicate detection, segmentation, and pose estimation. To address this, we introduce a synthetic dataset with 40,000 unique scenes of white Agaricus bisporus and brown baby bella mushrooms, capturing realistic variations in quantity, position, orientation, and growth stages. Our two-stage pose estimation pipeline combines 2D object detection and instance segmentation with a 3D point cloud-based pose estimation network using a Point Transformer. By employing a continuous 6D rotation representation and a geodesic loss, our method ensures precise rotation predictions. Experiments show that processing point clouds with 1024 points and the 6D Gram–Schmidt rotation representation yields optimal results, achieving an average rotational error of 1.67° on synthetic data, surpassing current state-of-the-art methods in mushroom pose estimation. The model, further, generalizes well to real-world data, attaining a mean angle difference of 3.68° on a subset of the M18K dataset with ground-truth annotations. This approach aims to drive automation in harvesting, growth monitoring, and quality assessment in the mushroom industry. Full article
(This article belongs to the Special Issue Advanced Image Processing and Computer Vision (2nd Edition))
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