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 1734

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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
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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

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Keywords

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

Published Papers (2 papers)

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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
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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
Viewed by 793
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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