Session Ⅰ- Remote Sensing Image Intelligent Interpretation（DDL：2024.10.15）
Chairs: Qiang Li, Xidian University, China
Chao Shen, Xi'an Technological University, China
Keywords:
Remote Sensing
Deep Learning
Image Analysis
Computer Vision
Summary:
Remote sensing image intelligent interpretation is a key technology of geographic information science. It utilizes advanced algorithms such as computer vision and artificial intelligence to automatically and efficiently analyze and understand mass data from remote sensing images obtained by satellites, aircraft or UAV, and provide accurate land surface change information. It plays an important role in many fields, such as environmental protection, urban planning, disaster response, etc. Currently, due to the progress of data acquisition, images from different sources present new characteristics, i.e., large-scale, high-dimensional and complex structures. How to analyze these different types of data has become the crucial technique to improve the accuracy and comprehension of scene analysis and understanding. Meanwhile, the existing methods are difficult to accurately analyze and understand the scene in the complex and changeable environment. To solve these challenges, we invite researchers from academia and industry to publish original research on the technologies and applications related to remote sensing image intelligent interpretation. Its aims to explore more accurate and intelligent image interpretation methods, so as to promote the development of related fields.
Topics:
Multi-source image fusion
Image matching
Image quality enhancement
Geographic information extraction, such as road, building, water body, etc.
Object detection and recognition
Change detection and anomaly detection
Scene analysis and understanding
Multi-modal learning
Session Ⅱ- Advanced Machine Learning Technologies and Their Applications in Image Restoration and Intelligent Imaging（DDL：2024.10.15）
Chairs: Licheng Liu, Hunan University, China
Yunyi Li, Hunan University of Science and Technology, China
Keywords:
Machine Learning
Image Restoration
Intelligent Imaging
Summary:
Image restoration and intelligent imaging is one of the challenging tasks in the image processing and computer vision society. Powered by the advanced machine learning techniques, such technology in inverse problem has attracted increasing attention in recent years due to its wide range of applications, such as face image reconstruction, medical imaging, hyperspectral image restoration, snapshot compressive imaging, machine vision sensing, etc. Although the increasing number of related applications and achievements, many challenging problems remain unsolved, and some new problems emerge, such as image prior modeling, large-scale optimization, fast and robust algorithms. There is ample room for improvement in contemporary theories, methodologies, and applications for image restoration and intelligent imaging.
The aim of this workshop will discuss new machine learning technologies and their applications in image restoration and intelligent imaging. The topics include but not limited to new deep learning techniques, low-level image processing, restoration, and enhancement, intelligent imaging and sensing systems, signal processing and multi-sensor imaging fusion.
Session Ⅲ - Visual, Image and Signal Processing Innovative Application in Education（DDL：2024.10.15）
Chair: Juxiang Zhou, Yunnan Normal University, China
Keywords:
Computer Vision
Image Processing
Signal Processing
Multimodal Learning
Educational Technology
Innovative Applications
Summary:
This session aims to explore innovative applications of computer vision, image processing, multimodal learning and signal processing in the field of education. These technologies offer new possibilities for educational research and practice, enhancing teaching quality, learning experiences, and personalized education outcomes.
Topics:
Student behavior recognition, analysis, and intervention techniques based on computer vision
Innovative applications of signal processing in educational assessment and feedback systems
Methods and practices of multimodal data fusion in educational data analysis
Applications of multimodal learning analytics technologies in assessing student classroom participation and classroom management
Real-time learning monitoring and feedback in smart classrooms using image and video understanding
Adaptive generation of educational content and interaction design in multimodal learning environments
Cross-modal affective analysis and personalized learning support systems
Applications of speech signal processing in language learning and pronunciation improvement
Applications of biosignal processing in emotion recognition and learning state monitoring
Applications of biosignal processing in studying learning behaviors
Session Ⅳ - Pattern Recognition in Computer Vision（DDL：2024.10.25）
Chair: Hewei Yu, South China University of Technology, China
Keywords:
Computer Vision
Pattern Recognition
Image Classification
Object Recognition
Action Recognition
Summary:
In the field of computer vision, pattern recognition is widely applied in image analysis, object detection, scene understanding, autonomous driving, and other areas. With the rapid advancement of cutting-edge technologies such as deep learning and machine learning, pattern recognition technology has made significant progress. In this session, we will focus on the latest research achievements and applications of pattern recognition in computer vision, aiming to provide a platform for communication and collaboration between academia and industry.
Topics:
Image Classification and Segmentation
Object Detection and Recognition
Pose Estimation and Action Recognition
Scene Understanding and Semantic Segmentation
Session Ⅴ - ISAR Imaging and Information Acquisition（DDL：2024.11.30）
Chair: Shuai Shao, Xidian University, China
Co-chairs: Shaopeng Wei, China University of Petroleum (East China), China
Jingyue Lu, Xidian University, China
Keywords:
ISAR Imaging
Radar Signal Processing
Motion Compensation
Feature Extraction
Target Recognition
Summary:
Inverse Synthetic Aperture Radar (ISAR) imaging technology can acquire two-dimensional (2-D) images of non-cooperative targets, and by integrating techniques such as sequence imaging, interferometric processing, and array signal processing, it can further obtain three-dimensional (3-D) images of the targets. From 2-D and 3-D images, multidimensional characteristic information of the observed targets, such as geometric dimensions, shape contours, component structures, motion states, and spatial attitude, can be extracted, which is of great significance in the fields of classification, recognition, situation awareness and task judgment of non-cooperative targets.
Topics (The call for papers for this topic includes, but is not limited to):
Inverse Synthetic Aperture Radar (ISAR) Imaging
Three-Dimensional (3-D) Radar Imaging
Motion Compensation
Feature Extraction
Target Recognition
Session Ⅵ - Target Detection and Measurement Based on Computer Vision（DDL：2024.11.10）
Chair: Zhisheng Gao, Xihua University, China
Keywords:
Image Reconstruction
Visual Measurement
Target Detection
Action Recognition
Pose Estimation
Summary:
Computer vision has a wide range of applications in the industrial field, including denoising, inpainting and restoration of observed images in complex scenes, high-precision positioning and detection of specific targets, target posture measurement and estimation, and target action detection and recognition. This session provides a platform for communication and sharing of the application results of computer vision in the industrial field.
Topics:
Degraded Observation Image Reconstruction
Target Detection and Tracking
Posture Measurement and Recognition
Industrial Defect Detection and Recognition
Session Ⅶ - The Application of Artificial Intelligence in the Field of Meteorology（DDL：2024.11.30）
Chair: Jing Hu, Chengdu University of Information Technology, China
Keywords:
Climate Data Processing
Weather Forecasting
Climate Modeling
Extreme Weather Prediction
Summary:
Artificial Intelligence (AI) is revolutionizing the meteorological field by enhancing weather prediction accuracy, extreme weather event detection, and climate modeling. The integration of AI technologies like machine learning and deep learning with traditional meteorological data sources—such as radar, satellite, and ground observation systems—allows for improved decision-making and forecasting. However, significant challenges remain, such as addressing model biases, the interpretability of AI predictions, and limitations in the quality and availability of data. Moreover, the ethical implications of AI-driven decision-making in weather and climate forecasting present additional hurdles. Overcoming these challenges requires innovative approaches and collaborative research efforts.
To address these evolving challenges, we invite researchers from academia and industry to publish original research on the technologies and applications related to AI in meteorology. This call aims to explore more accurate, efficient, and intelligent approaches in this critical field
Topics:
Data Fusion and Processing in Meteorology
AI in Weather Forecasting
Extreme Weather Event Prediction
Session Ⅷ - Intelligent algorithms and advanced control and their applications（DDL：2024.12.15）
Chair: Lei Liu, Liaoning University of Technology, China
Keywords:
Reinforcement learning
Deep learning
Multi-agent systems
Neural networks
Fuzzy logic systems
Intelligent control
Summary:
With the advances of technology, classic control algorithms have gradually shown limitations in many practical problems, especially when dealing with highly complex, nonlinear, uncertain, or high-dimensional problems. Hence, intelligent algorithms have emerged. Intelligent algorithms do not rely on accurate mathematical models, but perform intelligent search, adaptation, learning, and optimization by mimicking the natural heuristic behavior of organisms. It demonstrates unique advantages in dealing with nonlinear, dynamic, and high-dimensional problems that traditional algorithms cannot efficiently solve. Advanced control technology is an extension of traditional control technology, aimed at addressing systems with high uncertainty, time-varying nature, and complexity, and improving system stability and robustness. Intelligent algorithms and advanced control technologies are often complementary in modern engineering practice. Intelligent algorithms can be used to optimize control strategies, enhance the autonomy of control design or the adaptability of system parameter adjustment, while advanced control methods can ensure the stability, robustness, and efficiency of the system in practical applications. The purpose of this special session is to present the latest achievements in intelligent algorithms and advanced control, with the aim of gathering experts and scholars in the fields of intelligent algorithms and advanced control to discuss the practicality, latest progress, and challenges faced by relevant solutions, and to brainstorm new solutions and directions.
Topics:
Model-based reinforcement learning
Deep reinforcement learning
Multi-agent systems
Sliding mode controller design
T-S models
Lyapunov function
Adaptive fuzzy controller design
Neural network models
Intelligent algorithm and adaptive control
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