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: closed (30 November 2024) | Viewed by 1007

Special Issue Editors


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Guest Editor
School of Aerospace Science and Technology, Xidian University, Xi’an 710126, China
Interests: pose estimation; visual measurement; symmetric feature extraction; calibration; SLAM
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444,China
Interests: Robot positioning and perception; machine vision measurement;

E-Mail Website
Guest Editor
School of Electrical Engineering & Automation, Harbin Institute of Technology, No.92 Dazhi Road, Harbin 150000, China
Interests: precision motion control; photoelectric information conversion and processing based on embedded system computer vision

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

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Keywords

  • image process

  • machine learning
  • machine vision

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Published Papers (1 paper)

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Research

15 pages, 7490 KiB  
Article
Online Trajectory Optimization of an ASV Based on an Improved RBFNN
by Suyu Yan and Chao Ming
Symmetry 2024, 16(12), 1679; https://doi.org/10.3390/sym16121679 - 18 Dec 2024
Viewed by 459
Abstract
Optimization based on conventional neural networks can only guarantee calculation speed. However, the accuracy of network prediction is also a key index in online trajectory optimization. This paper proposes a novel online trajectory optimization approach based on an Improved Radial Basis Function Neural [...] Read more.
Optimization based on conventional neural networks can only guarantee calculation speed. However, the accuracy of network prediction is also a key index in online trajectory optimization. This paper proposes a novel online trajectory optimization approach based on an Improved Radial Basis Function Neural Network (IRBFNN). First, multiple optimal trajectories for different variations of Air-Breathing Supersonic Vehicles (ASVs) are generated, considering attitude, and collected as the dataset. Then, an IRBFNN is trained to predict trajectories online with the above dataset, in which an enhanced loss function is introduced to improve prediction accuracy. Finally, numerical simulations are presented to demonstrate the feasibility and superiority of the proposed online trajectory optimization method. The open-source code of the proposed method is shared in the form of a link to showcase its implementation. Full article
(This article belongs to the Special Issue Application of Image Processing with Symmetry/Asymmetry)
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