Application of Image Processing with Symmetry/Asymmetry
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 314
Special Issue Editors
Interests: pose estimation; visual measurement; symmetric feature extraction; calibration; SLAM
Interests: Robot positioning and perception; machine vision measurement;
Special Issue Information
Dear Colleagues,
Image processing is widely applied in various industries, such as medical image processing, video surveillance, robot vision, automated detection, etc. Digital images undergo processing to obtain effective information, and symmetry is a very important concept within this. The application of image processing is largely influenced by different types of spatial symmetry, such as axial symmetry, translational symmetry, and rotational symmetry. The discovery of symmetry can extract more features from images. Therefore, image processing algorithms and applications based on symmetry have become a research hotspot this year. In this Special Issue of Symmetry, the focus of this topic is to fully leverage the advantages of symmetry theory in image processing applications.
Dr. Jiashan Cui
Dr. Yunhui Li
Prof. Dr. Ju Huo
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.
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Keywords
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image process
- machine learning
- machine vision
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: A Novel Lightweight Sonar Image Object Detection Model Based on Adaptive Focus Modulation Cross-Stage Multi-Scale Attention Network
Authors: Kun Zheng1,2, Hong-Seng Gan3, Jun Kit Chaw1, * , Sze-Hong Teh4,*and Zhe Chen2
Affiliation: 1 Institute of Visual Informatics, National University of Malaysia (UKM), 43600 Bangi, Selangor, Malaysia.
2 School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
3The School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong – Liverpool University, 215400 Suzhou City, Jiangsu Province, China
4School of Intelligent Manufacturing Ecosystem, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong - Liverpool University, 215400 Suzhou City, Jiangsu Province, China
Abstract: Abstract: With the development of marine resources and artificial intelligence (AI) technology, the importance of automatic underwater target detection has become evident. Sonar image processing is crucial for this task. Although deep learning has improved traditional detection methods, challenges remain due to sonar images' low resolution and high noise levels, resulting in large model sizes and low recognition accuracy. To tackle these issues, this paper proposes a lightweight sonar image detection model based on the Adaptive Focus Modulation Cross-Stage Feature Enhancement Network. First, it introduces a Cross-stage Multi-scale Contextual Attention Network (CMCANet) to leverage multi-scale attention modules, reducing network depth and parameter sizes. Second, the original SPPF is replaced with a focus modulation network to enhance target localization within sonar images. Finally, an improved Adaptive Spatial Feature Fusion Module is used during prediction to augment the model's receptive field and detection accuracy by integrating physical and semantic features for multi-level target detection. Experimental results demonstrate that the proposed model surpasses existing methods in terms of parameter sizes, detection accuracy, and speed. This research provides novel insights for the design of future sonar image target detection models.
