Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.2
Journals
Symmetry
Special Issues
Computer Vision, Robotics, and Automation Engineering
share announcement

Computer Vision, Robotics, and Automation Engineering

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

Deadline for manuscript submissions: 31 March 2027 | Viewed by 2059

Special Issue Editors

Prof. Dr. Jie Yang

E-Mail Website
Guest Editor
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200400, China
Interests: artificial intelligence; machine learning; computer vision; medical image analysis and 3D construction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jian Cheng

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: machine learning; computer vision; remote sensing image analysis; multimodal image classification; video surveillance and scene understanding

Special Issue Information

Dear Colleagues,

This Special Issue will focus on symmetry in computer vision, robotics, and automation engineering, and present the research results on the key challenges and research directions faced by the development of this field, thereby promoting the development and application of theories and technologies within this field.

Topics of interest include, but are not limited to, the following:

Intelligent Control Systems and Optimization

  • Genetic Algorithm;
  • Fuzzy Control;
  • Decision Support Systems;
  • Machine Learning in Control Applications;
  • Knowledge-based System Applications;
  • Hybrid Learning Systems;
  • Distributed Control Systems;
  • Evolutionary Computation and Control;
  • Optimization Algorithms;
  • Intelligent Algorithms and Systems;
  • Soft Computing;
  • Software Agents for Intelligent Control Systems;
  • Neural Network-based Control Systems;
  • Planning and Scheduling.

Robotics and Automation

  • Image Processing;
  • Vision, Recognition, and Reconstruction;
  • Robot Design, Development, and Control;
  • Control and Monitoring Systems;
  • Telerobotics and Teleoperation;
  • Humanoid Robotics Intelligence;
  • Object Recognition and Tracking;
  • Sensor Fusion;
  • Industrial Networks and Automation;
  • Assistive Robotics;
  • Autonomous Robotics;
  • Bionic Robotics;
  • Biomechanics;
  • Biomedical Robotics;
  • Biomimetic Robotics;
  • Distributed Sensing;
  • Haptic Feedback;
  • Multi-Agent Systems;
  • Multimedia Robotics.

Computer Vision and Recognition

  • Active and Robotic Vision;
  • Biometric Authentication;
  • Camera Networks and Vision;
  • Cognitive and Bio-inspired Vision;
  • Fuzzy and Neural Techniques in Vision;
  • Face and Gesture Recognition;
  • Image Feature Extraction;
  • Novel Vision Applications and Case Studies;
  • Machine Learning Techniques for Vision;
  • Object Recognition;
  • Sensors and Early Vision.

The content of submitted paper should be related to SymmetryWe look forward to your submissions.

Prof. Dr. Jie Yang
Prof. Dr. Jian Cheng
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 250 words) can be sent to the Editorial Office for assessment.

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

  • artificial intelligence
  • computer vision
  • robotics
  • automation
  • image processing
  • pattern recognition

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 5876 KB  
Article
A Stacking-Based Ensemble Learning Method for Multispectral Reconstruction of Printed Halftone Images
by Lin Zhu, Jinghuan Ge, Dongwen Tian and Jie Yang
Symmetry 2026, 18(3), 406; https://doi.org/10.3390/sym18030406 - 25 Feb 2026
Viewed by 446
Abstract
Motivation: Accurate spectral reconstruction of printed halftone images is essential for achieving high-fidelity color reproduction and robust color management across modern printing systems. However, traditional physics-based models, such as the Yule–Nielsen and Clapper–Yule formulations, rely on simplified empirical assumptions and often fail to [...] Read more.
Motivation: Accurate spectral reconstruction of printed halftone images is essential for achieving high-fidelity color reproduction and robust color management across modern printing systems. However, traditional physics-based models, such as the Yule–Nielsen and Clapper–Yule formulations, rely on simplified empirical assumptions and often fail to capture the complex nonlinear and asymmetric interactions induced by multi-ink overlays and substrate light scattering. Meanwhile, existing data-driven approaches based on single learning models exhibit limited capability in modeling the complementary and symmetrical characteristics inherent in halftone structures, resulting in suboptimal prediction accuracy and generalization performance. Method: To address these limitations, we propose a Stacking Ensemble Spectral Prediction (SESP) framework. The proposed method adopts a two-layer stacking architecture that integrates heterogeneous base regressors, including Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost 3.0.3), with Ridge Regression employed as the meta-learner for optimal prediction aggregation. This ensemble design enables effective modeling of both halftone pattern symmetry and complex substrate scattering behavior. Results: Extensive experiments conducted on printed halftone image datasets demonstrate the superior performance of the proposed SESP framework. Compared with the best-performing reference method (PCA-IPSO-DNN), SESP achieves relative reductions in RMSE and CIEDE2000 of 12.8% and 6.8% under illuminant A, 9.5% and 6.9% under D50, and 12.2% and 7.2% under D65, respectively. In addition, SESP consistently outperforms traditional physics-based models, including Yule–Nielsen and Clapper–Yule, in terms of both spectral prediction accuracy and colorimetric fidelity. These results confirm the effectiveness of the proposed framework in modeling the intricate nonlinear and asymmetric relationships between CMYK halftone patterns and spectral reflectance. Full article
(This article belongs to the Special Issue Computer Vision, Robotics, and Automation Engineering)
►▼ Show Figures

Figure 1

23 pages, 11516 KB  
Article
Symmetry-Constrained Multi-Camera Tracking for Aircraft Preflight Inspection via Spatio-Temporal Graph Optimization
by Wanli Dang, Jian Cheng, Jiang Wang, Huaiyu Zheng, Qian Luo, Chao Wang and Ping Zhang
Symmetry 2026, 18(2), 387; https://doi.org/10.3390/sym18020387 - 22 Feb 2026
Viewed by 519
Abstract
Automated verification of preflight aircraft inspection—a critical safety procedure—is addressed by integrating multi-camera tracking with procedural knowledge through a symmetry-aware spatio-temporal graph model. Departing from conventional tracking paradigms, the framework encodes operational protocols and structural symmetries of the aircraft as explicit constraints for [...] Read more.
Automated verification of preflight aircraft inspection—a critical safety procedure—is addressed by integrating multi-camera tracking with procedural knowledge through a symmetry-aware spatio-temporal graph model. Departing from conventional tracking paradigms, the framework encodes operational protocols and structural symmetries of the aircraft as explicit constraints for trajectory association. Semantically consistent inspection zones are derived from geometric symmetry, and reliable tracklets extracted within them are connected using rules that enforce temporal order and identity consistency. Verification is formulated as a constrained shortest-path search over this graph, ensuring sequential and complete coverage of all mandatory zones by a single inspector. Evaluated on real-world airport surveillance data across diverse conditions, the proposed approach achieves a Complete Inspection Success Rate of 86.5%, significantly outperforming state-of-the-art tracking and re-identification baselines. The results demonstrate that explicit procedural integration substantially enhances the reliability and interpretability of automated compliance verification in safety-critical industrial monitoring. Full article
(This article belongs to the Special Issue Computer Vision, Robotics, and Automation Engineering)
►▼ Show Figures

Figure 1

24 pages, 5044 KB  
Article
Research on Fouling Shellfish on Marine Aquaculture Cages Detection Technology Based on an Improved Symmetric Faster R-CNN Detection Algorithm
by Pengshuai Zhu, Hao Li, Junhua Chen and Chengjun Guo
Symmetry 2025, 17(12), 2107; https://doi.org/10.3390/sym17122107 - 8 Dec 2025
Cited by 1 | Viewed by 560
Abstract
The development of detection and identification technologies for biofouling organisms on marine aquaculture cages is of paramount importance for the automation and intelligence of cleaning processes by Autonomous Underwater Vehicles (AUVs). The present study proposes a methodology for the detection of fouling shellfish [...] Read more.
The development of detection and identification technologies for biofouling organisms on marine aquaculture cages is of paramount importance for the automation and intelligence of cleaning processes by Autonomous Underwater Vehicles (AUVs). The present study proposes a methodology for the detection of fouling shellfish on marine aquaculture cages. This methodology is based on an improved version of a symmetric Faster R-CNN: The original Visual Geometry Group 16-layer (VGG16) network is replaced with a 50-layer Residual Network with Aggregated Transformations (ResNeXt50) architecture, incorporating a Convolutional Block Attention Module (CBAM) to enhance feature extraction capabilities; In addition, the anchor box dimensions must be optimised concurrently with the Intersection over Union (IoU) threshold. This is to ensure the adaptation to the scale of the object; combined with the Multi-Scale Retinex with Single Scale Component and Color Restoration (MSRCR) algorithm with a view to achieving image enhancement. Experiments demonstrate that the enhanced model attains an average precision of 94.27%, signifying a 10.31% augmentation over the original model whilst necessitating a mere one-fifth of the original model’s weight. At an intersection-over-union (IoU) value of 0.5, the model attains a mean average precision (mAP) of 93.14%, surpassing numerous prevalent detection models. Furthermore, the employment of an image-enhanced dataset during the training of detection models has been demonstrated to yield an average precision that is 11.72 percentage points higher than that achieved through training with the original dataset. In summary, the technical approach proposed in this paper enables accurate and efficient detection and identification of fouling shellfish on marine aquaculture cages. Full article
(This article belongs to the Special Issue Computer Vision, Robotics, and Automation Engineering)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop