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Symmetry
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Asymmetric and Symmetric Study on Image Processing and Statistical Data...
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Asymmetric and Symmetric Study on Image Processing and Statistical Data Analysis

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 12348

Special Issue Editors

Prof. Dr. Wei Feng

E-Mail Website
Guest Editor
School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, China
Interests: remote sensing; machine learning; pattern recognition; image processing; data mining
Prof. Dr. Qiang Li

E-Mail Website
Guest Editor
College of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China
Interests: particle physics; hadron phenomenology; technology of dectector measurement
Dr. Gabriel Dauphin

E-Mail Website
Guest Editor
Institut Galilée, Villetaneuse, France
Interests: remote sensing and classification

Special Issue Information

Dear Colleagues,

Image processing has been widely used in forestry, agriculture, geology, mineral resources, hydrology and water resources, ocean, environmental monitoring, and other aspects, and has made a great contribution to the global economic and social development. With the development of information technology, some traditional image processing methods can no longer meet the needs of the current social development. Machine learning has become a popular image and data processing method. It can quickly and timely obtain a large amount of accurate objective information, which is incomparable by traditional methods. However, this kind of technology still faces many challenges, such as computation complexity, low accuracy, low generalization, etc. Giving full play to the advantages of symmetry theory in image processing and data analysis are intensively pursued in this special section.

Prof. Dr. Wei Feng
Prof. Dr. Qiang Li
Dr. Gabriel Dauphin
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. Symmetry 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 2400 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
  • machine learning
  • ecological environment
  • object identification
  • remote sensing

Published Papers (7 papers)

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Research

24 pages, 1399 KiB  
Article
The Unit Alpha-Power Kum-Modified Size-Biased Lehmann Type II Distribution: Theory, Simulation, and Applications
by Rabab S. Gomaa, Alia M. Magar, Najwan Alsadat, Ehab M. Almetwally and Ahlam H. Tolba
Symmetry 2023, 15(6), 1283; https://doi.org/10.3390/sym15061283 - 19 Jun 2023
Cited by 1 | Viewed by 914
Abstract
In order to represent the data with non-monotonic failure rates and produce a better fit, a novel distribution is created in this study using the alpha power family of distributions. This distribution is called the alpha-power Kum-modified size-biased Lehmann type II or, in [...] Read more.
In order to represent the data with non-monotonic failure rates and produce a better fit, a novel distribution is created in this study using the alpha power family of distributions. This distribution is called the alpha-power Kum-modified size-biased Lehmann type II or, in short, the AP-Kum-MSBL-II distribution. This distribution is established for modeling bounded data in the interval (0,1). The proposed distribution’s moment-generating function, mode, quantiles, moments, and stress–strength reliability function are obtained, among other attributes. To estimate the parameters of the proposed distribution, estimation methods such as the maximum likelihood method and Bayesian method are employed to estimate the unknown parameters for the AP-Kum-MSBL-II distribution. Moreover, the confidence intervals, credible intervals, and coverage probability are calculated for all parameters. The symmetric and asymmetric loss functions are used to find the Bayesian estimators using the Markov chain Monte Carlo (MCMC) method. Furthermore, the proposed distribution’s usefulness is demonstrated using three real data sets. One of them is a medical data set dealing with COVID-19 patients’ mortality rate, the second is a trade share data set, and the third is from the engineering area, as well as extensive simulated data, which were applied to assess the performance of the estimators of the proposed distribution. Full article
(This article belongs to the Special Issue Asymmetric and Symmetric Study on Image Processing and Statistical Data Analysis)
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18 pages, 1683 KiB  
Article
A Novel Cluster Matching-Based Improved Kernel Fisher Criterion for Image Classification in Unsupervised Domain Adaptation
by Siraj Khan, Muhammad Asim, Samia Allaoua Chelloug, Basma Abdelrahiem, Salabat Khan and Ahmad Musyafa
Symmetry 2023, 15(6), 1163; https://doi.org/10.3390/sym15061163 - 28 May 2023
Cited by 2 | Viewed by 1294
Abstract
Unsupervised domain adaptation (UDA) is a popular approach to reducing distributional discrepancies between labeled source and the unlabeled target domain (TD) in machine learning. However, current UDA approaches often align feature distributions between two domains explicitly without considering the target distribution and intra-domain [...] Read more.
Unsupervised domain adaptation (UDA) is a popular approach to reducing distributional discrepancies between labeled source and the unlabeled target domain (TD) in machine learning. However, current UDA approaches often align feature distributions between two domains explicitly without considering the target distribution and intra-domain category information, potentially leading to reduced classifier efficiency when the distribution between training and test sets differs. To address this limitation, we propose a novel approach called Cluster Matching-based Improved Kernel Fisher criterion (CM-IKFC) for object classification in image analysis using machine learning techniques. CM-IKFC generates accurate pseudo-labels for each target sample by considering both domain distributions. Our approach employs K-means clustering to cluster samples in the latent subspace in both domains and then conducts cluster matching in the TD. During the model component training stage, the Improved Kernel Fisher Criterion (IKFC) is presented to extend cluster matching and preserve the semantic structure and class transitions. To further enhance the performance of the Kernel Fisher criterion, we use a normalized parameter, due to the difficulty in solving the characteristic equation that draws inspiration from symmetry theory. The proposed CM-IKFC method minimizes intra-class variability while boosting inter-class variants in all domains. We evaluated our approach on benchmark datasets for UDA tasks and our experimental findings show that CM-IKFC is superior to current state-of-the-art methods. Full article
(This article belongs to the Special Issue Asymmetric and Symmetric Study on Image Processing and Statistical Data Analysis)
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18 pages, 4114 KiB  
Article
Robust Image Hashing Using Histogram Reconstruction for Improving Content Preservation Resistance and Discrimination
by Yao Jia, Chen Cui and Ahmed A. Abd El-Latif
Symmetry 2023, 15(5), 1088; https://doi.org/10.3390/sym15051088 - 15 May 2023
Viewed by 1409
Abstract
This paper proposes a new image hashing method, which uses histogram reconstruction to solve the problem of the histogram not being sensitive to the change of pixel position, while ensuring the robustness of the hashing algorithm against common content preservation attacks (such as [...] Read more.
This paper proposes a new image hashing method, which uses histogram reconstruction to solve the problem of the histogram not being sensitive to the change of pixel position, while ensuring the robustness of the hashing algorithm against common content preservation attacks (such as blurring, noise addition and rotation). The proposed algorithm can resist arbitrary angles of rotation, possibly because the reconstructed histogram leverages the rotational symmetry and its own invariance to rotation operations. We measure the similarity between different images by calculating the Hamming distance of the hash vectors of different images. Our experiments show that the proposed method performs well in robustness and discrimination compared with other established algorithms. In addition, we conduct a receiver operating characteristic curve analysis to further verify the superior overall performance of our image hash method. Full article
(This article belongs to the Special Issue Asymmetric and Symmetric Study on Image Processing and Statistical Data Analysis)
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14 pages, 8496 KiB  
Article
Spectral-Spatial Feature Enhancement Algorithm for Nighttime Object Detection and Tracking
by Yan Lv, Wei Feng, Shuo Wang, Gabriel Dauphin, Yali Zhang and Mengdao Xing
Symmetry 2023, 15(2), 546; https://doi.org/10.3390/sym15020546 - 17 Feb 2023
Cited by 1 | Viewed by 1592
Abstract
Object detection and tracking has always been one of the important research directions in computer vision. The purpose is to determine whether the object is contained in the input image and enclose the object with a bounding box. However, most object detection and [...] Read more.
Object detection and tracking has always been one of the important research directions in computer vision. The purpose is to determine whether the object is contained in the input image and enclose the object with a bounding box. However, most object detection and tracking methods are applied to daytime objects, and the processing of nighttime objects is imprecise. In this paper, a spectral-spatial feature enhancement algorithm for nighttime object detection and tracking is proposed, which is inspired by symmetrical neural networks. The proposed method consists of the following steps. First, preprocessing is performed on unlabeled nighttime images, including low-light enhancement, object detection, and dynamic programming. Second, object features for daytime and nighttime times are extracted and modulated with a domain-adaptive structure. Third, the Siamese network can make full use of daytime and nighttime object features, which is trained as a tracker by the above images. Fourth, the test set is subjected to feature enhancement and then input to the tracker to obtain the final detection and tracking results. The feature enhancement step includes low-light enhancement and Gabor filtering. The spatial-spectral features of the target are fully extracted in this step. The NAT2021 dataset is used in the experiments. Six methods are employed as comparisons. Multiple judgment indicators were used to analyze the research results. The experimental results show that the method achieves excellent detection and tracking performance. Full article
(This article belongs to the Special Issue Asymmetric and Symmetric Study on Image Processing and Statistical Data Analysis)
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12 pages, 3946 KiB  
Article
Co-Saliency Detection of RGBD Image Based on Superpixel and Hypergraph
by Weiyi Wei, Wenxia Chen and Mengyu Xu
Symmetry 2022, 14(11), 2393; https://doi.org/10.3390/sym14112393 - 12 Nov 2022
Cited by 2 | Viewed by 1059
Abstract
For the co-saliency detection algorithm of an RGBD image that may have incomplete detection of common salient regions and unclear boundaries, we proposed an improved co-saliency detection method of RGBD images based on superpixels and hypergraphs. First, we optimized the depth map based [...] Read more.
For the co-saliency detection algorithm of an RGBD image that may have incomplete detection of common salient regions and unclear boundaries, we proposed an improved co-saliency detection method of RGBD images based on superpixels and hypergraphs. First, we optimized the depth map based on edge consistency, and introduced the optimized depth map into the SLIC algorithm to obtain the better superpixel segmentation results of RGBD images. Second, the color features, optimized depth features and global spatial features of superpixels were extracted to construct a weighted hypergraph model to generate saliency maps. Finally, we constructed a weighted hypergraph model for co-saliency detection based on the relationship of color features, global spatial features, optimized depth features and saliency features among images. In addition, in order to verify the impact of the symmetry of the optimized depth information on the co-saliency detection results, we compared the proposed method with two types of models, which included considering depth information and not considering depth information. The experimental results on Cosal150 and Coseg183 datasets showed that our improved algorithm had the advantages of suppressing the background and detecting the integrity of the common salient region, and outperformed other algorithms on the metrics of P-R curve, F-measure and MAE. Full article
(This article belongs to the Special Issue Asymmetric and Symmetric Study on Image Processing and Statistical Data Analysis)
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13 pages, 3260 KiB  
Article
Radar-Jamming Classification in the Event of Insufficient Samples Using Transfer Learning
by Yanbin Hou, Huidong Ren, Qinzhe Lv, Lili Wu, Xiaodong Yang and Yinghui Quan
Symmetry 2022, 14(11), 2318; https://doi.org/10.3390/sym14112318 - 04 Nov 2022
Cited by 6 | Viewed by 2073
Abstract
Radar has played an irreplaceable role in modern warfare. A variety of radar-jamming methods have been applied in recent years, which makes the electromagnetic environment more complex. The classification of radar jamming is critical for electronic counter-countermeasures (ECCM). In the field of signal [...] Read more.
Radar has played an irreplaceable role in modern warfare. A variety of radar-jamming methods have been applied in recent years, which makes the electromagnetic environment more complex. The classification of radar jamming is critical for electronic counter-countermeasures (ECCM). In the field of signal classification, machine learning-based methods take great effort to find proper features as well as classifiers, and deep learning-based methods depend on large training datasets. For the above reasons, an efficient transfer learning-based method is proposed in this paper. Firstly, one-dimensional radar signals were transformed into time–frequency images (TFIs) using linear and bilinear time–frequency analysis, which is inspired by symmetry theory. Secondly, pretrained AlexNet and SqueezeNet networks were modified to classify the processed TFIs. Finally, performance of this method was evaluated and compared using a simulated data set with nine types of radar-jamming signals. The results demonstrate that our proposed classification method performs well in accuracy and efficiency at a 1% training ratio, which is practical for anti-jamming. Full article
(This article belongs to the Special Issue Asymmetric and Symmetric Study on Image Processing and Statistical Data Analysis)
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13 pages, 3151 KiB  
Article
LPI Radar Signal Recognition Based on Dual-Channel CNN and Feature Fusion
by Daying Quan, Zeyu Tang, Xiaofeng Wang, Wenchao Zhai and Chongxiao Qu
Symmetry 2022, 14(3), 570; https://doi.org/10.3390/sym14030570 - 14 Mar 2022
Cited by 15 | Viewed by 2534
Abstract
The accuracy of low probability of intercept (LPI) radar waveform recognition is an important and challenging problem in electronic warfare. Aiming at the problem of the difficulty in feature extraction and the low recognition rates of the LPI radar signal under a low [...] Read more.
The accuracy of low probability of intercept (LPI) radar waveform recognition is an important and challenging problem in electronic warfare. Aiming at the problem of the difficulty in feature extraction and the low recognition rates of the LPI radar signal under a low signal-to-noise ratio, and inspired by the symmetry theory, we propose a new approach for the LPI radar signal recognition method based on a dual-channel convolutional neural network (CNN) and feature fusion. Our new approach contains three main modules: the preprocessing module that converts the LPI radar waveforms into two-dimensional time-frequency images using the Choi–Williams distribution (CWD) transformation and performs image binarization, the feature extraction module that extracts different features obtained from the images, and the recognition module that utilizes a multi-layer perceptron (MLP) network to fuse these features and distinguish the type of LPI radar signals. In the feature extraction module, a two-channel CNN model is proposed that extracts Histogram of Oriented Gradients (HOG) features and deep features from time-frequency images, respectively. Finally, the recognition module recognizes the radar signals using a Softmax classifier based on the fused features from two channels. The experimental results from 12 types of LPI radar signals prove the superiority and robustness of the proposed model. Its overall recognition rate reaches 97% when the signal-to-noise ratio is −6 dB. Full article
(This article belongs to the Special Issue Asymmetric and Symmetric Study on Image Processing and Statistical Data Analysis)
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