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Special Issue "Asymmetric and Symmetric Study on Image Processing and Statistical Data Analysis"

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

Deadline for manuscript submissions: 31 March 2023 | Viewed by 4062

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Special Issue Editors

Prof. Dr. Wei Feng

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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

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Guest Editor

College of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China
Interests: particle physics; hadron phenomenology; technology of dectector measurement
Special Issues, Collections and Topics in MDPI journals

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 2000 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 (5 papers)

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Research

Open AccessArticle

A Novel Heteromorphic Ensemble Algorithm for Hand Pose Recognition

and

Symmetry 2023, 15(3), 769; https://doi.org/10.3390/sym15030769 - 21 Mar 2023

Viewed by 280

Abstract

Imagining recognition of behaviors from video sequences for a machine is full of challenges but meaningful. This work aims to predict students’ behavior in an experimental class, which relies on the symmetry idea from reality to annotated reality centered on the feature space. A heteromorphic ensemble algorithm is proposed to make the obtained features more aggregated and reduce the computational burden. Namely, the deep learning models are improved to obtain feature vectors representing gestures from video frames and the classification algorithm is optimized for behavior recognition. So, the symmetric idea is realized by decomposing the task into three schemas including hand detection and cropping, hand joints feature extraction, and gesture classification. Firstly, a new detector method named YOLOv4-specific tiny detection (STD) is proposed by reconstituting the YOLOv4-tiny model, which could produce two outputs with some attention mechanism leveraging context information. Secondly, the efficient pyramid squeeze attention (EPSA) net is integrated into EvoNorm-S0 and the spatial pyramid pool (SPP) layer to obtain the hand joint position information. Lastly, the D–S theory is used to fuse two classifiers, support vector machine (SVM) and random forest (RF), to produce a mixed classifier named S–R. Eventually, the synergetic effects of our algorithm are shown by experiments on self-created datasets with a high average recognition accuracy of 89.6%. Full article

(This article belongs to the Special Issue Asymmetric and Symmetric Study on Image Processing and Statistical Data Analysis)

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Open AccessArticle

Spectral-Spatial Feature Enhancement Algorithm for Nighttime Object Detection and Tracking

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Symmetry 2023, 15(2), 546; https://doi.org/10.3390/sym15020546 - 17 Feb 2023

Viewed by 505

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 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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Open AccessArticle

Co-Saliency Detection of RGBD Image Based on Superpixel and Hypergraph

Weiyi Wei

Wenxia Chen

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Mengyu Xu

Symmetry 2022, 14(11), 2393; https://doi.org/10.3390/sym14112393 - 12 Nov 2022

Cited by 1 | Viewed by 525

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 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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Open AccessArticle

Radar-Jamming Classification in the Event of Insufficient Samples Using Transfer Learning

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Symmetry 2022, 14(11), 2318; https://doi.org/10.3390/sym14112318 - 04 Nov 2022

Cited by 1 | Viewed by 773

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 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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Open AccessArticle

LPI Radar Signal Recognition Based on Dual-Channel CNN and Feature Fusion

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Symmetry 2022, 14(3), 570; https://doi.org/10.3390/sym14030570 - 14 Mar 2022

Cited by 5 | Viewed by 1293

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 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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Special Issue "Asymmetric and Symmetric Study on Image Processing and Statistical Data Analysis"

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