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Mathematics
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Advanced Research in Image Processing and Optimization Methods
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Advanced Research in Image Processing and Optimization Methods

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3270

Special Issue Editor

Prof. Dr. Juan Gabriel Avina-Cervantes

E-Mail Website
Guest Editor
Enginieering Division of the Campus Irapuato-Salamanca, University of Guanajuato, Salamanca 36885, Mexico
Interests: computer vision; pattern recognition; optimization methods; automatic control; machine and deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image processing techniques have been developed to solve many practical science and engineering problems, supported by proper mathematical foundations. Fortunately, the gamut of applications has broadened while sharing techniques with other dynamic domains, such as pattern recognition, mainly classical and modern optimization algorithms. The current trends of image processing include, non-exhaustively, image enhancement and restoration, object segmentation, information extraction, text recognition, object classification and recognition, robot localization and mapping, encryption and steganography, image inpainting and 3D reconstruction, and scene modelling, among others. Many applications of image processing have since emerged, some of them contributing to human health by detecting and classifying sensitive or abnormal structures in biomedical images. The accuracy and effectiveness of image processing technical have been substantially improved, including the well-known optimization methods, starting with the traditional gradient-based methods up to the most modern metaheuristic and hyperheuristic optimization methods. In this context, this Special Issue is now open to receiving high-quality papers for reviewing and publishing the accepted manuscripts in close-related areas to advanced research in image processing and optimization methods. 

Prof. Dr. Juan Gabriel Avina-Cervantes
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. 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

  • digital image processing
  • optimization methods
  • mathematical modelling
  • pattern recognition
  • machine and deep learning

Published Papers (4 papers)

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Research

23 pages, 3898 KiB  
Article
Enhanced Classification of Human Fall and Sit Motions Using Ultra-Wideband Radar and Hidden Markov Models
by Thottempudi Pardhu, Vijay Kumar, Andreas Kanavos, Vassilis C. Gerogiannis and Biswaranjan Acharya
Mathematics 2024, 12(15), 2314; https://doi.org/10.3390/math12152314 - 24 Jul 2024
Viewed by 395
Abstract
In this study, we address the challenge of accurately classifying human movements in complex environments using sensor data. We analyze both video and radar data to tackle this problem. From video sequences, we extract temporal characteristics using techniques such as motion history images [...] Read more.
In this study, we address the challenge of accurately classifying human movements in complex environments using sensor data. We analyze both video and radar data to tackle this problem. From video sequences, we extract temporal characteristics using techniques such as motion history images (MHI) and Hu moments, which capture the dynamic aspects of movement. Radar data are processed through principal component analysis (PCA) to identify unique detection signatures. We refine these features using k-means clustering and employ them to train hidden Markov models (HMMs). These models are tailored to distinguish between distinct movements, specifically focusing on differentiating sitting from falling motions. Our experimental findings reveal that integrating video-derived and radar-derived features significantly improves the accuracy of motion classification. Specifically, the combined approach enhanced the precision of detecting sitting motions by over 10% compared to using single-modality data. This integrated method not only boosts classification accuracy but also extends the practical applicability of motion detection systems in diverse real-world scenarios, such as healthcare monitoring and emergency response systems. Full article
(This article belongs to the Special Issue Advanced Research in Image Processing and Optimization Methods)
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20 pages, 2540 KiB  
Article
Feature Fusion-Based Re-Ranking for Home Textile Image Retrieval
by Ziyi Miao, Lan Yao, Feng Zeng, Yi Wang and Zhiguo Hong
Mathematics 2024, 12(14), 2172; https://doi.org/10.3390/math12142172 - 11 Jul 2024
Viewed by 273
Abstract
In existing image retrieval algorithms, negative samples often appear at the forefront of retrieval results. To this end, in this paper, we propose a feature fusion-based re-ranking method for home textile image retrieval, which utilizes high-level semantic similarity and low-level texture similarity information [...] Read more.
In existing image retrieval algorithms, negative samples often appear at the forefront of retrieval results. To this end, in this paper, we propose a feature fusion-based re-ranking method for home textile image retrieval, which utilizes high-level semantic similarity and low-level texture similarity information of an image and strengthens the feature expression via late fusion. Compared with single-feature re-ranking, the proposed method combines the ranking diversity of multiple features to improve the retrieval accuracy. In our re-ranking process, Markov random walk is used to update the similarity metrics, and we propose local constraint diffusion based on contextual similarity. Finally, the fusion–diffusion algorithm is used to optimize the sorted list via combining multiple similarity metrics. We set up a large-scale home textile image dataset, which contains 89k home textile product images from 12k categories, and evaluate the image retrieval performance of the proposed model with the Recall@k and mAP@K metrics. The experimental results show that the proposed re-ranking method can effectively improve the retrieval results and enhance the performance of home textile image retrieval. Full article
(This article belongs to the Special Issue Advanced Research in Image Processing and Optimization Methods)
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11 pages, 2609 KiB  
Article
Transfer Learning with ResNet3D-101 for Global Prediction of High Aerosol Concentrations
by Dušan P. Nikezić, Dušan S. Radivojević, Ivan M. Lazović, Nikola S. Mirkov and Zoran J. Marković
Mathematics 2024, 12(6), 826; https://doi.org/10.3390/math12060826 - 12 Mar 2024
Cited by 2 | Viewed by 893
Abstract
In order to better predict the high aerosol concentrations associated with air pollution and climate change, a machine learning model was developed using transfer learning and the segmentation process of global satellite images. The main concept of transfer learning lies on convolutional neural [...] Read more.
In order to better predict the high aerosol concentrations associated with air pollution and climate change, a machine learning model was developed using transfer learning and the segmentation process of global satellite images. The main concept of transfer learning lies on convolutional neural networks and works by initializing the already trained model weights to better adapt the weights when the network is trained on a different dataset. The transfer learning technique was tested with the ResNet3D-101 model pre-trained from a 2D ImageNet dataset. This model has performed well for contrail detection to assess climate impact. Aerosol distributions can be monitored via satellite remote sensing. Satellites can monitor some aerosol optical properties like aerosol optical thickness. Aerosol optical thickness snapshots were the input dataset for the model and were obtained from NASA’s Terra-Modis satellite; the output images were segmented by comparing the pixel values with a threshold value of 0.8 for aerosol optical thickness. Hyperparameter optimization finds a tuple of hyperparameters that yields an optimal model that minimizes a predefined loss function on given independent data. The model structure was adjusted in order to improve the performance of the model by applying methods and hyperparameter optimization techniques such as grid search, batch size, threshold, and input length. According to the criteria defined by the authors, the distance domain criterion and time domain criterion, the developed model is capable of generating adequate data and finding patterns in the time domain. As observed from the comparison of relative coefficients for the criteria metrics proposed by the authors, ddc and dtc, the deep learning model based on ConvLSTM layers developed in our previous studies has better performance than the model developed in this study with transfer learning. Full article
(This article belongs to the Special Issue Advanced Research in Image Processing and Optimization Methods)
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17 pages, 1635 KiB  
Article
EEG-BCI Features Discrimination between Executed and Imagined Movements Based on FastICA, Hjorth Parameters, and SVM
by Tat’y Mwata-Velu, Armando Navarro Rodríguez, Yanick Mfuni-Tshimanga, Richard Mavuela-Maniansa, Jesús Alberto Martínez Castro, Jose Ruiz-Pinales and Juan Gabriel Avina-Cervantes
Mathematics 2023, 11(21), 4409; https://doi.org/10.3390/math11214409 - 24 Oct 2023
Cited by 1 | Viewed by 1075
Abstract
Brain–Computer Interfaces (BCIs) communicate between a given user and their nearest environment through brain signals. In the case of device handling, an accurate control-based BCI depends essentially on how the user performs corresponding mental tasks. In the BCI illiteracy-related literature, one subject could [...] Read more.
Brain–Computer Interfaces (BCIs) communicate between a given user and their nearest environment through brain signals. In the case of device handling, an accurate control-based BCI depends essentially on how the user performs corresponding mental tasks. In the BCI illiteracy-related literature, one subject could perform a defined paradigm better than another. Therefore, this work aims to identify recorded Electroencephalogram (EEG) signal segments related to the executed and imagined motor tasks for BCI system applications. The proposed approach implements pass-band filters and the Fast Independent Component Analysis (FastICA) algorithm to separate independent sources from raw EEG signals. Next, EEG features of selected channels are extracted using Hjorth parameters. Finally, a Support Vector Machines (SVMs)-based classifier identifies executed and imagined motor features. Concretely, the Physionet dataset, related to executed and imagined motor EEG signals, provided training, testing, and validating data. The numerical results let us discriminate between executed and imagined motor tasks accurately. Therefore, the proposed method offers a reliable alternative to extract EEG features for BCI based on executed and imagined movements. Full article
(This article belongs to the Special Issue Advanced Research in Image Processing and Optimization Methods)
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